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This monograph highlights the connection between the theoretical
work done by research statisticians and the impact that work has on
various industries. Drawing on decades of experience as an industry
consultant, the author details how his contributions have had a
lasting impact on the field of statistics as a whole. Aspiring
statisticians and data scientists will be motivated to find
practical applications for their knowledge, as they see how such
work can yield breakthroughs in their field. Each chapter
highlights a consulting position the author held that resulted in a
significant contribution to statistical theory. Topics covered
include tracking processes with change points, estimating common
parameters, crossing fields with absorption points, military
operations research, sampling surveys, stochastic visibility in
random fields, reliability analysis, applied probability, and more.
Notable advancements within each of these topics are presented by
analyzing the problems facing various industries, and how solving
those problems contributed to the development of the field. The
Career of a Research Statistician is ideal for researchers,
graduate students, or industry professionals working in statistics.
It will be particularly useful for up-and-coming statisticians
interested in the promising connection between academia and
industry.
This innovative textbook presents material for a course on modern
statistics that incorporates Python as a pedagogical and practical
resource. Drawing on many years of teaching and conducting research
in various applied and industrial settings, the authors have
carefully tailored the text to provide an ideal balance of theory
and practical applications. Numerous examples and case studies are
incorporated throughout, and comprehensive Python applications are
illustrated in detail. A custom Python package is available for
download, allowing students to reproduce these examples and explore
others. The first chapters of the text focus on analyzing
variability, probability models, and distribution functions. Next,
the authors introduce statistical inference and bootstrapping, and
variability in several dimensions and regression models. The text
then goes on to cover sampling for estimation of finite population
quantities and time series analysis and prediction, concluding with
two chapters on modern data analytic methods. Each chapter includes
exercises, data sets, and applications to supplement learning.
Modern Statistics: A Computer-Based Approach with Python is
intended for a one- or two-semester advanced undergraduate or
graduate course. Because of the foundational nature of the text, it
can be combined with any program requiring data analysis in its
curriculum, such as courses on data science, industrial statistics,
physical and social sciences, and engineering. Researchers,
practitioners, and data scientists will also find it to be a useful
resource with the numerous applications and case studies that are
included. A second, closely related textbook is titled Industrial
Statistics: A Computer-Based Approach with Python. It covers topics
such as statistical process control, including multivariate
methods, the design of experiments, including computer experiments
and reliability methods, including Bayesian reliability. These
texts can be used independently or for consecutive courses. The
mistat Python package can be accessed at
https://gedeck.github.io/mistat-code-solutions/ModernStatistics/
"In this book on Modern Statistics, the last two chapters on modern
analytic methods contain what is very popular at the moment,
especially in Machine Learning, such as classifiers, clustering
methods and text analytics. But I also appreciate the previous
chapters since I believe that people using machine learning methods
should be aware that they rely heavily on statistical ones. I very
much appreciate the many worked out cases, based on the
longstanding experience of the authors. They are very useful to
better understand, and then apply, the methods presented in the
book. The use of Python corresponds to the best programming
experience nowadays. For all these reasons, I think the book has
also a brilliant and impactful future and I commend the authors for
that." Professor Fabrizio RuggeriResearch Director at the National
Research Council, ItalyPresident of the International Society for
Business and Industrial Statistics (ISBIS)Editor-in-Chief of
Applied Stochastic Models in Business and Industry (ASMBI)
This monograph highlights the connection between the theoretical
work done by research statisticians and the impact that work has on
various industries. Drawing on decades of experience as an industry
consultant, the author details how his contributions have had a
lasting impact on the field of statistics as a whole. Aspiring
statisticians and data scientists will be motivated to find
practical applications for their knowledge, as they see how such
work can yield breakthroughs in their field. Each chapter
highlights a consulting position the author held that resulted in a
significant contribution to statistical theory. Topics covered
include tracking processes with change points, estimating common
parameters, crossing fields with absorption points, military
operations research, sampling surveys, stochastic visibility in
random fields, reliability analysis, applied probability, and more.
Notable advancements within each of these topics are presented by
analyzing the problems facing various industries, and how solving
those problems contributed to the development of the field. The
Career of a Research Statistician is ideal for researchers,
graduate students, or industry professionals working in statistics.
It will be particularly useful for up-and-coming statisticians
interested in the promising connection between academia and
industry.
Reliability analysis is concerned with the analysis of devices and
systems whose individual components are prone to failure. This
textbook presents an introduction to reliability analysis of
repairable and non-repairable systems. It is based on courses given
to both undergraduate and graduate students of engineering and
statistics as well as in workshops for professional engineers and
scientists. As aresult, the book concentrates on the methodology of
the subject and on understanding theoretical results rather than on
its theoretical development. An intrinsic aspect of reliability
analysis is that the failure of components is best modelled using
techniques drawn from probability and statistics. Professor Zacks
covers all the basic concepts required from these subjects and
covers the main modern reliability analysis techniques thoroughly.
These include: the graphical analysis of life data, maximum
likelihood estimation and bayesian likelihood estimation.
Throughout the emphasis is on the practicalities of the subject
with numerous examples drawn from industrial and engineering
settings.
A large number of papers have appeared in the last twenty years on
estimating and predicting characteristics of finite populations.
This monograph is designed to present this modern theory in a
systematic and consistent manner. The authors' approach is that of
superpopulation models in which values of the population elements
are considered as random variables having joint distributions.
Throughout, the emphasis is on the analysis of data rather than on
the design of samples. Topics covered include: optimal predictors
for various superpopulation models, Bayes, minimax, and maximum
likelihood predictors, classical and Bayesian prediction intervals,
model robustness, and models with measurement errors. Each chapter
contains numerous examples, and exercises which extend and
illustrate the themes in the text. As a result, this book will be
ideal for all those research workers seeking an up-to-date and
well-referenced introduction to the subject.
Fully revised and updated, this book combines a theoretical
background with examples and references to R, MINITAB and JMP,
enabling practitioners to find state-of-the-art material on both
foundation and implementation tools to support their work. Topics
addressed include computer-intensive data analysis, acceptance
sampling, univariate and multivariate statistical process control,
design of experiments, quality by design, and reliability using
classical and Bayesian methods. The book can be used for workshops
or courses on acceptance sampling, statistical process control,
design of experiments, and reliability. Graduate and post-graduate
students in the areas of statistical quality and engineering, as
well as industrial statisticians, researchers and practitioners in
these fields will all benefit from the comprehensive combination of
theoretical and practical information provided in this single
volume. Modern Industrial Statistics: With applications in R,
MINITAB and JMP: * Combines a practical approach with theoretical
foundations and computational support. * Provides examples in R
using a dedicated package called MISTAT, and also refers to MINITAB
and JMP. * Includes exercises at the end of each chapter to aid
learning and test knowledge. * Provides over 40 data sets
representing real-life case studies. * Is complemented by a
comprehensive website providing an introduction to R, and
installations of JMP scripts and MINITAB macros, including
effective tutorials with introductory material:
www.wiley.com/go/modern-industrial-statistics.
The present monograph is a comprehensive summary of the research on
visibility in random fields, which I have conducted with the late
Professor Micha Yadin for over ten years. This research, which
resulted in several published papers and technical reports (see
bibliography), was motivated by some military problems, which were
brought to our attention by Mr. Pete Shugart of the US Army TRADOC
Systems Analysis Activity, presently called US Army TRADOC Analysis
Command. The Director ofTRASANA at the time, the late Dr. Wilbur
Payne, identified the problems and encouraged the support and
funding of this research by the US Army. Research contracts were
first administered through the Office of Naval Research, and
subsequently by the Army Research Office. We are most grateful to
all involved for this support and encouragement. In 1986 I
administered a three-day workshop on problem solving in the area of
sto chastic visibility. This workshop was held at the White Sands
Missile Range facility. A set of notes with some software were
written for this workshop. This workshop led to the incorporation
of some of the methods discussed in the present book into the Army
simulation package CASTFOREM. Several people encouraged me to
extend those notes and write the present monograph on the level of
those notes, so that the material will be more widely available for
applications."
This monograph is focused on the derivations of exact distributions
of first boundary crossing times of Poisson processes, compound
Poisson processes, and more general renewal processes. The content
is limited to the distributions of first boundary crossing times
and their applications to various stochastic models. This book
provides the theory and techniques for exact computations of
distributions and moments of level crossing times. In addition,
these techniques could replace simulations in many cases, thus
providing more insight about the phenomenona studied. This book
takes a general approach for studying telegraph processes and is
based on nearly thirty published papers by the author and
collaborators over the past twenty five years. No prior knowledge
of advanced probability is required, making the book widely
available to students and researchers in applied probability,
operations research, applied physics, and applied mathematics.
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