Statistical Modeling And Computation

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Statistical Modeling And Computation

Author : Dirk P. Kroese
ISBN : 9781461487753
Genre : Computers
File Size : 52.46 MB
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This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.​
Category: Computers

Statistical Modeling Linear Regression And Anova A Practical Computational Perspective

Author : Hamid Ismail
ISBN : 138720551X
Genre : Reference
File Size : 77.59 MB
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Statistical modeling is a branch of advanced statistics and a critical component of many applications in science and business. This book is an attempt to satisfy the need of mathematical statisticians and computational students in linear modeling and ANOVA. This book addresses linear modeling from a computational perspective with an emphasis on the mathematical details and step-by-step calculations using SAS(R) PROC IML. This book covers correlation analysis, simple and multiple linear regression, polynomial regression, regression with correlated data, model selection, analysis of covariance (ANCOVA), and analysis of variance (ANOVA). The level is suitable for upper level undergraduate and graduate students with knowledge of linear algebra and some programming skills.
Category: Reference

Complex Models And Computational Methods In Statistics

Author : Matteo Grigoletto
ISBN : 9788847028715
Genre : Mathematics
File Size : 26.56 MB
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The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
Category: Mathematics

An Introduction To Statistical Modeling Of Extreme Values

Author : Stuart Coles
ISBN : 9781447136750
Genre : Mathematics
File Size : 87.13 MB
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Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.
Category: Mathematics

Statistical Modeling And Machine Learning For Molecular Biology

Author : Alan Moses
ISBN : 9781482258608
Genre : Mathematics
File Size : 29.44 MB
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Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.
Category: Mathematics

Advances In Complex Data Modeling And Computational Methods In Statistics

Author : Anna Maria Paganoni
ISBN : 9783319111490
Genre : Mathematics
File Size : 88.2 MB
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The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.
Category: Mathematics

Bayesian Thinking Modeling And Computation

Author :
ISBN : 0080461174
Genre : Mathematics
File Size : 28.14 MB
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This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics
Category: Mathematics

Computational And Statistical Challenges In High Dimensional Statistical Models

Author : Ilias Zadik
ISBN : OCLC:1138021665
Genre :
File Size : 53.54 MB
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This thesis focuses on two long-studied high-dimensional statistical models, namely (1) the high-dimensional linear regression (HDLR) model, where the goal is to recover a hidden vector of coefficients from noisy linear observations, and (2) the planted clique (PC) model, where the goal is to recover a hidden community structure from a much larger observed network. The following results are established. First, under assumptions, we identify the exact statistical limit of the model, that is the minimum signal strength allowing a statistically accurate inference of the hidden vector. We couple this result with an all-or-nothing information theoretic (IT) phase transition. We prove that above the statistical limit, it is IT possible to almost-perfectly recover the hidden vector, while below the statistical limit, it is IT impossible to achieve non-trivial correlation with the hidden vector. Second, we study the computational-statistical gap of the sparse HDLR model; The statistical limit of the model is significantly smaller than its apparent computational limit, which is the minimum signal strength required by known computationally-efficient methods to perform statistical inference. We propose an explanation of the gap by analyzing the Overlap Gap Property (OGP) for HDLR. The OGP is known to be linked with algorithmic hardness in the theory of average-case optimization. We prove that the OGP for HDLR appears, up-to-constants, simultaneously with the computational-statistical gap, suggesting the OGP is a fundamental source of algorithmic hardness for HDLR. Third, we focus on noiseless HDLR. Here we do not assume sparsity, but we make a certain rationality assumption on the coefficients. In this case, we propose a polynomial-time recovery method based on the Lenstra-Lenstra-Lóvasz lattice basis reduction algorithm. We prove that the method obtains notable guarantees, as it recovers the hidden vector with using only one observation. Finally, we study the computational-statistical gap of the PC model. Similar to HDLR, we analyze the presence of OGP for the PC model. We provide strong (first-moment) evidence that again the OGP coincides with the model's computational-statistical gap. For this reason, we conjecture that the OGP provides a fundamental algorithmic barrier for PC as well, and potentially in a generic sense for high-dimensional statistical tasks.

Advances In Mathematical And Statistical Modeling

Author : Barry C. Arnold
ISBN : 0817646264
Genre : Mathematics
File Size : 79.33 MB
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Enrique Castillo is a leading figure in several mathematical and engineering fields. Organized to honor Castillo’s significant contributions, this volume is an outgrowth of the "International Conference on Mathematical and Statistical Modeling," and covers recent advances in the field. Applications to safety, reliability and life-testing, financial modeling, quality control, general inference, as well as neural networks and computational techniques are presented.
Category: Mathematics

Time Series

Author : Raquel Prado
ISBN : 9781420093360
Genre : Mathematics
File Size : 70.28 MB
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Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers. The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB® code, and other material are available on the authors’ websites. Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.
Category: Mathematics

Statistical Models In S

Author : T.J. Hastie
ISBN : 9781351414227
Genre : Mathematics
File Size : 33.92 MB
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Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.
Category: Mathematics

Statistical Modeling For Biological Systems

Author : Anthony Almudevar
ISBN : 9783030346751
Genre : Medical
File Size : 40.85 MB
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This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev’s many research interests including stochastic modeling and the analysis of micro-array data, and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. Part A consists of original research articles, which can be roughly grouped into four thematic areas: (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. Part B consists of methodological research reported as a short communication, ending with some personal reflections on research fields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei’s publications, complete as far as we know. The contributions in this book are written by Dr. Yakovlev’s collaborators and notable statisticians including former presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev’s research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev’s work and recognizes the legacy of his contributions in the biostatistics community.
Category: Medical

Advances In Statistical Modeling And Inference

Author : Vijay Nair
ISBN : 9789812708298
Genre : Mathematics
File Size : 57.6 MB
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There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies. These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods. These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical modeling and inference. Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners.
Category: Mathematics

Quantum Statistical Models Of Hot Dense Matter

Author : Arnold F. Nikiforov
ISBN : 9783764373467
Genre : Science
File Size : 69.88 MB
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This book studies the widely used theoretical models for calculating properties of hot dense matter. Calculations are illustrated by plots and tables, and they are compared with experimental results. The purpose is to help understanding of atomic physics in hot plasma and to aid in developing efficient and robust computer codes for calculating opacity and equations of state for arbitrary material in a wide range of temperatures and densities.
Category: Science

Applied Bayesian Modeling And Causal Inference From Incomplete Data Perspectives

Author : Andrew Gelman
ISBN : 047009043X
Genre : Mathematics
File Size : 59.98 MB
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This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
Category: Mathematics

Statistical Modeling For Degradation Data

Author : Ding-Geng (Din) Chen
ISBN : 9789811051944
Genre : Mathematics
File Size : 71.20 MB
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This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines such as reliability, public health sciences, and finance. For example, information on products’ reliability can be obtained by analyzing degradation data. In addition, statistical modeling and inference techniques have been developed on the basis of different degradation measures. The book brings together experts engaged in statistical modeling and inference, presenting and discussing important recent advances in degradation data analysis and related applications. The topics covered are timely and have considerable potential to impact both statistics and reliability engineering.
Category: Mathematics

Statistical Modeling In Biomedical Research

Author : Yichuan Zhao
ISBN : 9783030334161
Genre : Medical
File Size : 21.93 MB
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This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their applications Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its applications.
Category: Medical

Bayesian Analysis With Python

Author : Osvaldo Martin
ISBN : 9781789349665
Genre : Computers
File Size : 53.75 MB
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Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical models Find out how different models can be used to answer different data analysis questions Compare models and choose between alternative ones Discover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.
Category: Computers

Recent Advances In Mathematical And Statistical Methods

Author : D. Marc Kilgour
ISBN : 9783319997193
Genre : Computers
File Size : 66.24 MB
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This book focuses on the recent development of methodologies and computation methods in mathematical and statistical modelling, computational science and applied mathematics. It emphasizes the development of theories and applications, and promotes interdisciplinary endeavour among mathematicians, statisticians, scientists, engineers and researchers from other disciplines. The book provides ideas, methods and tools in mathematical and statistical modelling that have been developed for a wide range of research fields, including medical, health sciences, biology, environmental science, engineering, physics and chemistry, finance, economics and social sciences. It presents original results addressing real-world problems. The contributions are products of a highly successful meeting held in August 2017 on the main campus of Wilfrid Laurier University, in Waterloo, Canada, the International Conference on Applied Mathematics, Modeling and Computational Science (AMMCS-2017). They make this book a valuable resource for readers interested not only in a broader overview of the methods, ideas and tools in mathematical and statistical approaches, but also in how they can attain valuable insights into problems arising in other disciplines.
Category: Computers