Statistical Inference

This text is for a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square erro

Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorou

本書計劃對具備微積分知識並主修數學、統計、工程、和科學（包括資訊科學、生物科學、醫藥科學和管理科學）的學生介紹機率與統計推論。它嘗試呈現機率與統計推論的內涵並藉由大量的範例來介紹機率與統計推論之各式各樣的可能應用。 本書的前四章包含了大多數統計學家都相信提供了機率以及單變量和雙變量的離散型與連續型的機率分配之優良課程內容。在第5章裡，這些概念被推廣到許多隨機變數，尤其是那些互相獨立者。這重要的

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeli

Foundations of Inference in Natural Science

In Algebra of Probable Inference, Richard T. Cox develops and demonstrates that probability theory is the only theory of inductive inference that abides by logical consistency. Cox does so through a

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contrib

It is impossible to hold contradictory beliefs in mind together at once. Eric Marcus examines the nature of belief and inference, in light of the phenomenon of rational necessity, to reveal how the un

Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation.

Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation.

Causal Inference in Statistics: A Primer Judea Pearl,?Computer Science and Statistics, University of California Los Angeles, USA Madelyn Glymour,?Philosophy, Carnegie Mellon University, Pittsburgh, US

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide

Introduction to Statistical Investigations, 1st Edition leads readers to learn about the process of conducting statistical investigations from data collection, to exploring data, to statistical i

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever exp

For upper-level undergraduate courses in deterministic and stochastic signals and system engineeringAn Integrative Approach to Signals, Systems and InferenceSignals, Systems and Inference is a compreh

This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference

Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. Particular emphasis

Biological and Cultural Bases of Human Inference addresses the interface between social science and cognitive science. In this volume, Viale and colleagues explore which human social cognitive powers

Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which busine

Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which busine

Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem.

The application of statistical methods to physics is essential. This unique book on statistical physics offers an advanced approach with numerous applications to the modern problems students are confr

Methods for Estimation and Inference in Modern Econometrics provides a comprehensive introduction to a wide range of emerging topics, such as generalized empirical likelihood estimation and alternativ

This book provides a rigorous algebraic study of the most popular inference formalisms with a special focus on their wide application area, showing that all these tasks can be performed by a single ge

Because statistical confidentiality embraces the responsibility for both protecting data and ensuring its beneficial use for statistical purposes, those working with personal and proprietary data can

MaxEnt workshops are devoted to Bayesian inference and Maximum Entropy methods in sciences and engineering. This year's meeting was also encompassed all aspects of probabilistic inference such as foun

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods of parameter estimation and hypothesis testing. Simultaneous Inf

This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel

Models in Statistical Social Research provides a comprehensive insight of models used in statistical social research based on statistical data and methods. While traditionally understood statistical m

This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It

This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.

This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing me

Modern statistical methods use complex, sophisticated models that can lead to intractable computations. Saddlepoint approximations can be the answer. Written from the user's point of view, this book explains in clear language how such approximate probability computations are made, taking readers from the very beginnings to current applications. The core material is presented in chapters 1-6 at an elementary mathematical level. Chapters 7-9 then give a highly readable account of higher-order asymptotic inference. Later chapters address areas where saddlepoint methods have had substantial impact: multivariate testing, stochastic systems and applied probability, bootstrap implementation in the transform domain, and Bayesian computation and inference. No previous background in the area is required. Data examples from real applications demonstrate the practical value of the methods. Ideal for graduate students and researchers in statistics, biostatistics, electrical engineering, econometric

Biological and Cultural Bases of Human Inference addresses the interface between social science and cognitive science. In this volume, Viale and colleagues explore which human social cognitive powers

How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of Inference to the Best Explanation, we work out what to infer from the evidence by thinking ab

Sure-fire lessons and motivating activities—from Inference Charades to Cause-and-Effect Flip Books—help every learner master the essential comprehension skills of inferring and understandin

This book is intended for the statistician or student interested in becoming a statistical consultant and shows how to communicate statistical results effectively. It clearly illustrates what the con

This text concentrates on what can be achieved using the likelihood/Fisherian methods of taking into account uncertainty when studying a statistical problem. It takes the concept of the likelihood as

This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.

Statistical Parameters is a unique new guide to current statistical methods and research. Based on a series of interdisciplinary lectures for users of statistical methods in research and development,