JRC 2006
Planning
Information.
Tentative Invited Sessions:
|
Theme |
Organizer |
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Empowering Non-Statisticians with Statistical
Thinking and Statistical Tools What does it mean to be an "Empowering
Statistician"? Angie Patterson, GE’s
Global Research Abstract: The expectation to empower non-statisticians with
statistical thinking, methods and tools is enough to put statisticians
outside of their comfort zone. After all... how do you "deliver"
empowerment? And when/where did we (statisticians) get trained to do this?
Through a case study at General Electric, we'll discuss a model
for empowerment, the benefits, and the ongoing role of the
statistician. Can
Statisticians Be Effective in Educating Others in Statistical Thinking? Bill Parr. Abstract:
________________________________________________ Accepting Shewhart’s
Challenge – Developing Statistically-Minded Leaders Ronald D. Snee Principal, Process and Organizational Excellence Tunnell Consulting King of Prussia, PA
19406-1324
Snee@TunnellConsulting.com Abstract
Almost seven decades ago
Walter Shewhart challenged us with admonishment
that “The long-range contribution of statistics depends not so much on
getting a lot of highly trained statisticians into industry as it does in
creating a statistically minded generation
of physicists, chemists, engineers and others who will in any way have a hand
in developing and directing the productive processes of tomorrow." The
importance, benefits and implementation of such action is the subject of this
presentation. Attention is focused on how to develop statistically minded
leaders. “Leader” is interpreted broadly to mean anyone who is working to
improve on how the organization runs its business. Needs of the customer –
leaders of our organizations – are addressed. Benefits and fears on the parts
of non-statisticians and statisticians alike are discussed. It is argued that
one effective method for enabling leaders to make greater use of statistical
thinking is to promote the widespread use of the DMAIC approach – Define,
Measure, Analyze, Improve, and Control - to process improvement and problem
solving. |
Gwen Stimely,
Minitab, Inc. |
|
Statistics in As
Panelists: Veronica
Czitrom, PhD Fellow
of the American Statistical Association Statistical
Training and Consulting T
N Goh, PhD Director,
Quality and Professor,
Industrial & Systems Engineering Department Dennis
Lin, PhD University
Distinguished Professor Bovas
Abraham, Ph.D President,
International Society for Business and Industrial statistics Ai-Chu Wu, Ph. D. Statistical
Consulting |
Veronica Czitrom, Statistical Training and Consulting TN
Goh, Director, Quality and Professor,
Industrial & Systems Engineering Department |
|
Statistics in European Business and Industry (schedule
this session for the 8th or the 9th) The session aims to give an
overview of the increasing impact of statistical methods on business and
industry in Presentations: On the Reliability of
Repairable Systems: Methods and Applications
Fabrizio Ruggeri CNR-IMATI, President of the European
Network for Business and Industrial Statistics (ENBIS) Repairable systems subject to
minimal repair are those systems whose reliability is the same just before a
failure and after the corresponding repair. Failures of such systems are
often described by means of non-homogeneous Poisson processes (NHPP). We
present some results and illustrate them in some case studies. Hypothesis generation
in improvement projects
Jeroen de Mast Institute for Business and
Industrial Statistics of the University of In quality improvement
projects — such as Six Sigma projects — an exploratory phase can be
discerned, during which possible causes, influence factors or variation
sources are identified. In a later, confirmatory phase the effects of these
possible causes are experimentally verified. Whereas the confirmatory phase
is well understood, in both the statistical sciences and philosophy of
science, the exploratory phase is poorly understood. This paper aims to
provide a framework for the type of reasoning in the exploratory phase by
reviewing relevant theories in philosophy of science, artificial intelligence
and medical diagnosis. Data-driven, explanation-driven (or abductive) and coherence-driven discovery will be
discussed. Furthermore, the presentation provides a classification and
description of approaches that could be followed for the identification of
possible causes. Finally, the theory and practice of exploratory data
analysis will be briefly reviewed. Optimal two-level
split-plot designs
Peter Goos
(in joint work with J.M. Lucas) Split-plot designs are very often used in industrial experimentation, both advertently and inadvertently. The present paper focuses on the optimal design of two-level split-plot designs for the estimation of main effects. In doing so, the attention is not restricted to regular split-plot designs, but non-regular split-plot designs with odd and heterogeneous whole plot sizes are studied too. Simple strategies for outperforming completely randomized designs in terms of A-, D-, G- and V-efficiency are presented, and conditions for optimally arranging the runs of two-level designs in split-plot designs with given numbers and (even and odd) sizes of whole plots are given. |
Jeroen de Mast, IBIS UvA |
|
New Advancements in
Variation Modeling, Analysis and Control for Complex Systems
By Li Zeng
and Shiyu Zhou, University of Wisconsin |
Jianjun (Jan) Shi http://www-personal.engin.umich.edu/~shihang/index.html |
|
Computer Experiments and Model Validation in
Engineering Applications Input Uncertainty and
Potential-to-Validate: Sampling Plans for Max D. Morris, Leslie M. Moore, Los Alamos National Laboratory Michael D. McKay, Los Alamos
National Laboratory Abstract: In complex
settings, validation of mechanistic computer models is often difficult
because appropriate input values are not precisely known. Input uncertainty limits the degree to
which models can be realistically validated.
The most optimistic pre-assessment of model validity, or
“potential-to-validate,” is closely associated with ideas and indices used in
probabilistic sensitivity and uncertainty analysis. This talk will review a relevant
nonparametric sampling-based approach to sensitivity/uncertainty analysis of
computer models, and discuss recent work in input sampling plans that support
the assessment of potential-to-validate. Error budget for the validation of physics-based
predictive models Roger Ghanem,
John Red-Horse, Sandia National Laboratories, Alireza Doostan, Johns Hopkins University, Baltimore, MD. The significant recent
growth in computing resources has ushered in the new field of prediction
science where the objective has evolved from solving a system of governing
equations, to approximating reality. This new research program
encompasses the emerging fields of model validation and uncertainty
quantification. Essentially, one wishes to rely on available
knowledge to predict the future evolution of a design quantity, in a useful
manner. Available knowledge is typically in the form of physical
laws and experimental evidence. Stochastic
representations of both measurements and physical models presents a
possible venue for combining the two pieces of knowledge in an integral
manner. Benefits from such a representation include the ability to
allocate resources, in a rational manner, between experimental and numerical
efforts, as well as the ability to better mitigate risk in the design
process. A more tangible benefit will be a reduction in the cost
of production as reliance on full scale tests is significantly shifted to confident
reliance on predictive models. Another tangible benefit will be
the ability to meet design criteria with preset confidence. Recent research on the
Polynomial Chaos approach to stochastic computational mechanics has enabled
us to develop the concept of an error budget that permits the rational
allotment of prediction error to experimental, computational, statistical,
and modeling sources. This error budget is the natural evolution of
error estimation (originally developed for computational science) to the realm
of prediction science. This talk will review the
Polynomial Chaos Expansion (PCE) approach to the analysis of stochastic
systems. Particular attention will be given to the assumptions
underlying this approach as well as to the computational challenges facing
its implementation. Methods and algorithms will be detailed for
addressing both these issues, thus opening the way to the application of PCE
to very large scale engineering systems. The path from stochastic
predictions to model validation will also be outlined. Using Computer Experiments and Understanding the
Effect of Model Uncertainty in Engineering Design under Uncertainty Wei Chen, Northwestern University The effectiveness of using Computer Aided Engineering (CAE) tools to support design decisions is often hindered by the enormous computational costs of complex analysis models, especially when uncertainty is considered. Approximations of analysis models, also known as “metamodels” built upon computer experiments are widely used for design concept exploration and optimization. However, most existing approaches for metamodeling have been developed for deterministic optimization and are not applicable to design under uncertainty. In this talk, recent developments of using computer experiments and metamodels for engineering design under uncertainty are discussed. An efficient algorithm for constructing optimal design of computer experiments and the techniques for probabilistic sensitivity analysis (PSA) and uncertainty analysis (UA) via the use of metamodels are presented. We also present a methodology developed within a Baysian framework for quantifying the impact of interpolation uncertainty due to the use of metamodels in robust design. The Bayesian prediction interval approach provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments in robust design. |
Wei Chen, Northwestern University |
|
Robust parameter design and Variation Reduction
Title: Improving calibration systems
through designed experiments Abstract:
Taguchi (1987) advocates the use of designed experiments to improve
measurement and calibration systems. In this paper we study some statistical
aspects of the problem. An appropriate performance measure is derived that
provides us with a deeper insight into Taguchi's signal-to-noise ratio. Two
different modeling approaches, namely, performance measure modeling and
response modeling are considered. The proposed approaches are illustrated and
compared using an experiment on drive shaft imbalance. (Joint work with Arden
Miller and Tirthankar Dasgupta)
Title:
Adaptive OFAT Applied to Robust Parameter Design Abstract:
Previous investigations have explored the performance of
adaptive OFAT ("one factor at a time") experimentation establishing
that the method exploits main effects with high probability and also tends
to exploit two-factor interactions when they are large. The current study
applies these results to robust parameter design. A simple method is
proposed in which resolution III factorial designs are used for an outer
array of noise factors and adaptive OFAT is used for exploring control
factors. This approach exploits control by noise interactions with high
probability and also tends to exploit control by control by noise
interactions when they are large. Model-based assessments and case
studies suggest that this approach provides substantially more improvements
than alternatives with similar run size including crossed resolution III arrays and combined arrays with mininum
J-aberration. The approach also provides advantages in flexibility, use
of prior knowledge, and costs due to control factor changes.
Title: Variance Component
Decomposition and Diagnosis for Batch Manufacturing Processes using ANOVA Abstract: In
batch manufacturing processes, the total process variation is generally
decomposed into batch-by-batch variation and within-batch variation.
Since different variation components may be caused by different sources,
separation, testing and estimation of each variance component are essential
to the process improvement. Most of the previous SPC research emphasized
on reducing variations due to assignable causes by implementing control
charts for process monitoring. Different from this focus, this talk aims to
analyze and reduce inherent natural process variations by applying the ANOVA
method. The key issue of using the ANOVA method is how to develop
appropriate statistical models for all variation components of
interest. The paper provides a generic framework for
decomposition of three typical variation components in batch manufacturing
processes. For the purpose of variation root causes diagnosis, the
corresponding linear contrasts are defined to represent the possible site
variation patterns and the statistical nested effect models are developed
accordingly. It shows that the use of a full factor decomposition model
can expedite the determination of the number of nested effect models and the
model structure. Finally, an example is given for the variation
reduction in the screening conductive gridline printing process for solar
battery fabrication. |
Roshan Vengazhiyil, Georgia
Institute of Technology |
|
Bayesian Statistical Models in Pattern Recognition Bayesian Robust analysis for
Microarray large data sets Speaker: Pulak
Gosh: Parametric gaussian distribution is very popular in identifying differentially expressed genes in microarray studies. However, this assumption may not be true for various reasons. For example, if the data contain outliers and skewness test based on gaussian distribution is not robust. We develop a robust Bayesian model for testing differentially expressed genes. ____________________________________________ Bayesian Mixture model for healthcare expenditure" Munkin Murat: Abstract: This paper proposes a model
to identify the pure treatment effect of private medical insurance on
healthcare expenditure. We address two well-known challenges in dealing with
expenditure data in our analysis. First, the insurance status is a choice
variable and, therefore, modeling of endogeneity is
necessary. Secondly, expenditure data are usually characterized by skewed
distributions. We develop a finite mixture model with an unknown number of
components to fit such expenditure data patterns. The model is applied to
analyzing the effect of health insurance type on overall health care
expenditure. Regularized Mahalanobis
distance-based clustering with Genetic algorithm Speaker: James Wicker: Abstract: We propose a fast Genetic-Algorithm based
clustering method that can separate complex structures efficiently. While
this was based on the Genetic K-Means algorithm and the Hyperellipsoidal
Clustering (HEC) algorithm, we demonstrate how this algorithm can separate
more complex structures than the Genetic K-Means algorithm and converge
faster than other HEC algorithms. Performance of the algorithm is tested on
simulated and real data. _________________________________________________ A multi-stage steering algorithm for
Clustering large data sets Aruma Buddana, SOMS Department, Clustering analysis is a renowned data mining technique, which
involves dividing a large dataset into meaningful subclasses and thus
extracting hidden patterns among the objects. But real time spatial data may
not have any of this information available. The shape of the clusters can be
very arbitrary such as spherical, linear, ellipsoidal, elongated
and the clusters can be populated with as many as 100,000 points or as few as
10 points in a given time. An automated clustering algorithm may not be sufficient to
cluster this type of data. An iterative clustering algorithm along with the
capability of visual steering may be a good approach. We propose a new
iterative algorithm which is the combination of automated clustering methods
like the Bayesian clustering, detection of multivariate outliers, and the
visual clustering. Simulated data from a plasma experiment and real
astronomical data are used to test the performance of the algorithm. |
Halima Bensmail, |
|
Bayesian Advances in Experimental Design and
Analysis Speakers: Bradley
Jones - SAS Institute How Bayesian Thinking Can Help in
Designing Experiments The
suitability of any given experimental design depends on the complexity of the
model that proves to be adequate to describe the system being studied. In
factor screening studies, the researcher is not sure which or even how many
of the possible factors are driving the responses of interest. Given this
level of uncertainty giving much consideration to model complexity may seem
premature. Yet, choosing a design without any thought for how one is going to
fit the data one obtains is ill advised. What to do? The
Bayesian paradigm provides a structure for designing experiments when there are a multiplicity of possible models to consider. This
talk reviews some of the research applying Bayesian thinking for choosing
designs. Topics include the initial choice of a screening design,
augmentation of designs, and design for models that are nonlinear in the
parameters. ----------------------------------------------------------- Roshan
Joseph Vengazhiyil - Georgia Institute of
Technology Title: Design and Analysis of
Experiments Using Functionally Induced Priors ------------------------------------------------------------------- Roselinde Kessels (Catholic
BradleyJones (SAS Institute, US), Hans Nyquist( PeterGoos ( MartinaVandebroek(Catholic Title: Bayesian
optimal design of choice experiments |
Robert Mee,
|
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Reliability
Analysis
Reliability: the other dimension of quality During
the past twenty years, manufacturing industries have gone through a revolution
in the use of statistical methods for product quality. Tools for process
monitoring and, particularly experimental design, are much more commonly used
today to maintain and improve product quality. A natural extension of the
revolution in product quality is to turn focus to product reliability, which
is defined as quality over time. This has given rise to programs like Design
for Six Sigma. This talk discusses the relationship between engineering
quality and reliability, plus outlines the role of statistics and
statisticians in the field of reliability. A brief introduction to the
statistical tools used in engineering reliability is provided and some
predictions for the future of statistics in engineering reliability are made. Bayesian approach on software reliability growth
model
As far as the software system operates, it does not experience the
degradation process as does the hardware system. Instead, the software
system stops operating due to the faults latent in the system; and thus, the
software reliability can be improved by removing the faults during the
testing phase. The software reliability is defined as the probability
of no failure-occurrence during a certain length of mission period. The fault
detection and debugging process are essential to improve software
reliability. In this talk, we discuss a new software reliability growth
model, which is the extension of the one by Kimura, M., Toyota, T. and
Yamada, S. (1999), Economic Analysis of Software Release Problems with
Warranty Cost and Reliability Requirement, Reliability Engineering
& System Safety, vol. 66, pp.49-55, and apply the Bayesian
method to determine the optimal software release time, while minimizing the
expected total software cost. Under this growth model, we assume that the
intensity function is the mixture of reliability growth and constant
reliability after the software is released to the user at the end of testing
phase. To apply the Bayesian approach, we treat three parameters, the initial
number of faults in the software, the fault detection rate and the weighted
factor, as random variables and assign appropriate prior distributions.
Based on such an approach, we propose a Bayesian method to determine the best
possible software release time, plus provide comparison with the non-Bayesian
method. . Planning
of Accelerated Degradation Tests Considering Robust Design
of Manufacturing Quality Parameters Lingyan
Ruan and Jye-Chyi Lu Georgia Institute of Technology, Abstract Typical
experimental designs for accelerated life or degradation tests assume that
products are from the same manufacturing condition. In searching the best combination of
controllable manufacturing variables product reliability is as important as
quality characteristic, especially for electronic or semiconductor
devices. The literature in
experimental designs considering both quality and reliability metrics is
scarce. This presentation proposes a
framework of designing accelerated degradation tests for selecting
manufacturing controllable variables that lead to longest product percentile
lifetime, minimized variance of lifetime estimates and also least sensitivity
of environmental noise factors creating variation of product reliability.
|
Organizer:
Frank Guess, Chair:
Stuart Hunter, Professor Emeritus, |
|
Measurement Studies Must be June 7 The Comparison of Two Measurement
Devices Joseph
G. Voelkel Rochester Institute of Technology Abstract Measurement devices sometimes have no reference standards with
which they may be compared, and in these cases they are often compared to
each other. This frequently occurs when
a new type of device is built and is to be compared to the current best
device. Frequently-used
methods of comparison include regression, correlation, or the so-called
Bland-Altman plotting method. We review some of these, including any
shortcomings they may have. We also compare our problem to the Gage R&R
studies that are commonly performed in industry. Under standard
assumptions, we illustrate that the
problem is non-identifiable when each device can only make one measurement on
each unit. In the case where multiple measurements
can be made for each device, we show how the devices may be compared by a
sequence of likelihood-ratio tests. An example based on two devices that are
used to measure intra-ocular pressure of the human eye is used to illustrate
the technique. These methods and
many of the results we present, while not new, do not appear to be commonly
used The Use of Factor Relationship Diagrams to Illustrate
Implications of Restrictions on Randomization in the Unit Structure in
Industrial Experimentation By
Cheryl R Hild The
Industrial experiments
are often run in split plot mode for reasons of expediency. Often, the
experimenter is unaware of restrictions on the error structure, especially
when these restrictions are created by non-manipulated sources of variation
that are confounded with factor combinations. The Factor Relationship Diagram
(FRD) is a method of displaying the relationship between the manipulated and unmanipulated factors a priori to actually running a DOE.
Answers to specific engineering questions, driven by the FRDs
for various potential experimental strategies, can help the experimenter
select the appropriate strategy for the given situation. The degree of
certainty in the answers to these questions provides a mechanism for increasing
faith in conclusions concerning whole plot effects, regardless of the
availability of a formal statistical test. This paper examines a philosophical motivation and
methodology for using Factor Relationship Diagrams as a proactive method for developing
questions that, when answered a priori to experimentation, develop
understanding of those sources of variation contributing to this lack of
precision due to restrictions on randomization in the error structure. This
understanding can then lead to the selection of an appropriate experiment
that mitigates the risks associated with drawing conclusions regarding the
whole plot factors. An example is discussed based on the confounding of fixed
factors in the measurement process not explicitly identified in the
experimental strategy. A Bayesian Analysis of
Interval-Censored Failure Time Data with Measurement Error Sarah Michalak Michael Hamada Nicolas Hengartner Statistical Sciences Group,
Presenter: Sarah Michalak Statistical Sciences Group Los Alamos National Laboratory PO Box 1663, MS F600 Los Alamos, NM 87545 Tel: 505-667-2625 Email:
michalak@lanl.gov Measurement error may lead
to interval-censored failure data where the interval endpoints are not known
exactly. We consider data with this characteristic that were collected
during an experiment assessing the susceptibility of a memory device to soft
errors resulting from cosmic-ray induced neutrons. (A soft
error is a transient error, i.e., bit
flip, that causes no permanent damage to the memory device.) We use a Weibull model and
take a Bayesian approach to the analysis. |
Joseph Vockel,
Rochester Institute of Technology |
|
Design of Experiments for Discrete Event Simulation Controlled Sequential Factorial Design
for SImulation Factor Screening
We propose controlled sequential factorial
design for An Adaptive Method for Factor
Screening for Simulation Experiments
Russell
Cheng, and Sue Lewis,
DOE for Fitting Forward and Inverse
Simulation Metamodels Russell
Barton, Abstract: |
Bruce Ankenman,
Northwestern University |
|
Statistics
and Information Technology Mixture Modeling with Spatial Components for Active
Delay Tomography Earl Lawrence, Statistics
Group, The field of active network
tomography is concerned with the estimation of link-level performance
measures, e.g. delay distributions for packets on a link, based on measured end-to-end performance of
injected traffic, e.g. total path delay for a probe packet. One area of
continuing research is the choice of appropriate
distributional forms for estimating delay as many standard parametric distributions are
inappropriate. In particular, most parametric
distributions are inadequate for modeling the tail behavior of delay distributions. This talk will
explore the use of mixture modeling to overcome this limitation. Mixture
models provide a flexible tool for capturing overall shape and tail behavior.
Further, we will consider the modeling
of spatial correlations in order to account for traffic similarity on
neighboring links, a problem much ignored in the current literature. Applications to real and
simulated data will be considered. The Characteristics of Voice over IP Traffic Bowei Xi, Department of Statistics, Voice over Internet
Protocol (VoIP) is a new and fast developing technology. Voice data, traditionally carried by the
"public switched telephone network", are
transmitted along with other applications on the IP network. An empirical
study of the VoIP data collected from the Global Crossing Network is
presented. Several key factors that play a critical role in traffic engineering
are examined: the multiplexed packet process, call arrivals and duration distributions, and silence
suppression. They exhibit distinctively
different characteristics than the traditional telephone call traffic. Local-Vote Decision Fusion for Target Detection in
Wireless Sensor Networks. Natallia Katenka, Department of
Statistics, The In this talk, we examine
the problem of target detection by a wireless sensor network. Sensors acquire
measurements emitted from the target that are corrupted by noise and initially
make individual decisions about the presence/absence of the target. We
propose the Local-Vote Decision Fusion algorithm, in which sensors first
correct their decisions using decisions of neighboring sensors, and then make
a collective decision as a network. We show that, for a fixed system false
alarm, this local correction achieves significantly higher target detection
rate. We examine both distance- and nearest neighbor-based versions of the
algorithm for grid and random sensor deployments. Further, an explicit
formula that approximates the decision threshold for a given false alarm rate
is derived, using limit theorems for random fields. This is joint work with Liza Levina and George Michailidis |
George Michailidis,
|
|
Simulation
of Supply Chains Speaker: Kenneth Gilbert,, Abstract: This session is an experiential simulation of
a multi-stage supply chain and a tutorial on ARIMA models of supply
chains. It does not assume any prior knowledge of supply chain models
and requires only a rudimentary understanding of time series models. |
Kenneth Gilbert, |
|
Six Sigma: What is Missing? Speakers 1.
Roger Hoerl, GE Global Research
.Six Sigma has been a tremendously successful improvement
initiative for close to 20 years now. Despite its unparalleled success,
however, it has its limitations like any other initiative. In this talk
we will attempt to separate the hype from the facts, and better understand
what Six Sigma is and is not. Based on this analysis, we plan to
identify improvement opportunities, and suggest ways in which these
opportunities might be captured. In summary, it will be argued the Six
Sigma was never designed to be an overall quality management system, and even within the arena of
project-by- Doug Zahn,
Statistical Consultant and Coach |
William Parr, |
|
Computational Techniques for Statistical Inference Talk 1. Improvements on the ROC Curve: Skill Plots for
Forecast Evaluation. Dr. William M. Briggs 10021
wib2004@med.cornell.edu We start by reviewing the
ROC curve, a standard method in the literature for evaluating a diagnosis or forecast. Next, the
skill score and skill test of
Briggs and Ruppert(2005) are introduced and advantages of this new technique are discussed. With this background, we
apply the skill score to simple discrimination problems with a single variable. In this context,
we prove that the skill maximizing
decision rule for problems such as classifying patients with a
disease coincides with Bayes Rule for optimal classification. This same separation rule is also
indicated as optimal by ROC curve analysis. Finally, we address the
question of inference for this optimal point, called x_max, and construct two types of confidence intervals. The first interval is a likelihood ratio interval
based on inverting the skill test mentioned above. A
second interval based on bootstrapping a
logistic regression model is also introduced and a small
coverage study is performed to evaluate the precision of the estimated optimal cutoff.
Talk 2. Dr. Matthew Tom A Test for Two Poisson Poisson
Processes in the Presence of Background Events Testing whether the means
of two Poisson random variables have a fixed
ratio lambda is a well-known and solved problem. The model has sufficient statistics and
conditional inference is possible. If instead each Poisson mean is
a mixture of signal parameter and a known background noise parameter
then we lose sufficiency and testing the hypothesis that the signal
parameters are equal becomes more difficult. In this talk, we will look at
different exact tests we can use to compare signal parameters despite the
background noise. As an example, we will look at an application from cosmic
ray particle physics. Talk 3. Russell Zaretzki Department of Statistics, rzaretzk@utk.edu A Parametric Bootstrap Likelihood Ratio Statistic
for time censored data with applications in Reliability. Building on the work of Jeng, Lahiri and Meeker(2005),
we consider bootstrap based likelihood ratio inference for time censored
data. A simulation study based on
data from a Weibull distribution under Type I censoring computes
finite sample coverage probabilities for bootstrap based inference of a modified signed root
statistic. The results are contrasted with
the better performing methods discussed by Jeng,
Lahiri and Meeker such as the ordinary bootstrap
signed root statistic and the bootstrap-t. Heuristic explanations are
given to explain why the modified bootstrap may outperform the ordinary bootstrap. |
Russell
Zaretzki, |
|
Pannel Discussion: Role of Optimal Design for Quality Improvement in the 21st Century Chair: Robert Mee, ·
Introduction ·
Commentary Curtis
L. Carlson Professor of Operations and Management Science Chair,
Operations and Management Science Department ·
Commentary
·
Commentary
·
Commentary ·
Commentary:
·
Floor discussion |
Ramón V. León, |
|
Statistical Methods for Analysis of Microarray Data Speaker: Al Bartolucci,
Ph.D.
Al Bartolucci1,
David B. Allison1, Sejong Bae2, Karan P.
Singh2 1.
Section on
Statistical Genetics, Department of Biostatistics 2.
Department of
Biostatistics, Abstract: There
is no doubt that the analysis of microarray data remains a challenge as one
wish to investigate the possibility of expressive genes in a sample of
thousands of such genes. Naturally the issue of multiplicity arises as one
examines the significance of large numbers of genes. Recently, one of the
coauthors, DBA, and colleagues developed a mixed model approach to this very
problem with successful application to a mouse data model. In this particular
setting one circumvents the false positive issue using a mixture distribution
of the p-values. Simultaneously one addresses several issues such as 1)
whether we have any statistically significant evidence in any of the genes,
2) what is the best estimate of the number of genes in which there is a true
difference in gene expression?, 3) is there a threshold which signals a
criteria above which genes should be investigated further?, and 4) what is the
possible proportion of false negatives in those genes declared “not
interesting” ? This
paper investigates this procedure further and illustrates its usefulness and
relevance in the current work on microarray data analysis.
Yoonkyung
Lee, Ph.D. A Sparse Solution Approach to Gene Selection for
Cancer Diagnosis Using Microarray Data Abstract: Cancer diagnosis or
prognosis based on gene expression profiles has been studied as a potentially
more accurate means of predicting the disease status than standard methods
based on histological observations. Presence of much larger number of genes
than the sample size in the problem poses a challenge in building reliable
and interpretable classification schemes. This talk will present a sparse
solution approach for simultaneous gene selection and classification via
component penalization of Support Vector Machines. The proposed method
selects relevant genes in a principled way by taking into account their joint
effects, remedying the limitation of common approaches of filtering genes
marginally. Real data analysis will be given for illustration of the method
and related issues will be discussed. ______________________________________________
David B. Dahl, Ph.D. Using Clustering to Enhance Hypothesis Testing Both multiple hypothesis testing
and clustering have been the subjects of extensive research for genomic and
other high dimensional data, yet they have traditionally been treated
separately. We propose a hybrid statistical
methodology that uses clustering information to increase testing
sensitivity. A test for an object that
uses data from all objects clustered with it will be more sensitive than one
that uses data from this object in isolation.
While the true clustering is unknown, there is increased power if the
clustering can be estimated relatively well.
We first consider a simplified setting which compares the power of the
standard Z-test to the power of a test using an estimated cluster. Theoretical results show that if the
cluster is estimated sufficiently well, the new procedure is more
powerful. In the setting of gene
expression data, we develop a model-based analysis using a carefully
formulated conjugate Dirichlet process mixture
model. The model is able to borrow
strength from objects likely to be clustered.
Simulations reveal this new method performs substantially better than
its peers. The proposed model is illustrated on a large microarray dataset. ____________________________________________________
Statistical Tools are
Needed for Microarray Expression and Co-expression Information Arnold M. Saxton, Brynn
H. Voy and Michael A. Langston
Microarray technology provides a measure of the activity of
virtually all genes in an organism's genome, for example the estimated 25,000
genes in the mouse genome. Typical experiments produce millions of
observations, and a complex statistical process has evolved
Speaker:
A
Review of Evolving Clustering Methods for Microarray data Analysis Abstract: Microarray
data analysis involves various statistical and computational methods
including principal component analysis, k-means clustering, neural networks,
and self-organised maps. We review these methods
within the context of microarray data analysis. An application of evolving
clustering methods based on neural networks is discussed. |
Karan Singh Karan P. Singh, Ph.D. Professor and Chair Department of Biostatistics
and Epidemiology University of Phone: (817) 735-0490 Fax #: (817) 735-2314 E-mail Address: ksingh@hsc.unt.edu |
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Technometrics
Session. 1) Speaker: Mu Zhu Department
of Statistics and Actuarial Science m3zhu@uwaterloo.ca Title: LAGO: A Computationally Efficient
Approach for Statistical Detection Co-authors: Wanhua Su Department
of Statistics and Actuarial Science Hugh
A. Chipman Department
of Mathematics and Statistics Abstract:
We study a general class of statistical detection problems where the
underlying objective is to detect items belonging to a rare class from a very
large database. We propose a computationally efficient method to achieve this
goal. Our method consists of two steps. In the first step, we estimate the
density function of the rare class alone with an adaptive bandwidth kernel
density estimator. The adaptive choice of the bandwidth is inspired by the
ancient Chinese board game known today as Go. In the second step, we adjust
this density locally depending on the density of the background class nearby.
We show that the amount of adjustment needed in the second step is
approximately equal to the adaptive bandwidth from the first step, which
gives us additional computational savings. We name the resulting method LAGO
for “locally adjusted Go-kernel density estimator." We then apply LAGO
to a real drug discovery data set and compare its performance with a number
of existing and popular methods. _________________________________________________________- 2) Speaker: David Mease Department
of Marketing and DecisionSciences mease d@cob.sjsu.edu Title: Latin Hyper-Rectangle Sampling for
Computer Experiments Co-author: Derek Bingham Department
of Statistics and Actuarial Science dbingham@cs.sfu.ca Abstract:
Latin hypercube sampling is a popular method for evaluating the expectation
of functions in computer experiments. However, when the expectation of
interest is taken with respect to a non-uniform distribution, the usual
transformation to the probability space can cause relatively smooth functions
to become extremely variable in areas of low probability. Consequently, the
equal probability cells inherent in hypercube methods often tend to sample an insufficient proportion of the total points in
these areas. In this talk we introduce Latin hyper-rectangle sampling to
address this problem. Latin hyper-rectangle sampling is a generalization of
Latin hypercube sampling which allows for non-equal cell probabilities. A
number of examples are given illustrating the improvement of the proposed
methodology over Latin hypercube sampling with respect to the variance of the
resulting estimators. Extensions to orthogonal-array based Latin hypercube sampling, stratified Latin hypercube sampling and
scrambled nets are also described. |
Randy
Sitter <sitter@cs.sfu.ca> Technometrics Editor |