Statistical Inference

This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, chang

This book explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations, in partic

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated

This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This

Exact statistical inference may be employed in diverse fields of science and technology. As problems become more complex and sample sizes become larger, mathematical and computational difficulties can arise that require the use of approximate statistical methods. Such methods are justified by asymptotic arguments but are still based on the concepts and principles that underlie exact statistical inference. With this in perspective, this book presents a broad view of exact statistical inference and the development of asymptotic statistical inference, providing a justification for the use of asymptotic methods for large samples. Methodological results are developed on a concrete and yet rigorous mathematical level and are applied to a variety of problems that include categorical data, regression, and survival analyses. This book is designed as a textbook for advanced undergraduate or beginning graduate students in statistics, biostatistics, or applied statistics but may also be used as a

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.This book describes a general and flexible framework for model

An exploration of the statistical foundations of scientific inference, The Nature of Scientific Evidence asks what constitutes scientific evidence and whether scientific evidence can be quantified sta

An exploration of the statistical foundations of scientific inference, The Nature of Scientific Evidence asks what constitutes scientific evidence and whether scientific evidence can be quantified sta

This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behav

過去50年經營管理的反思——如何轉危為安？ 品管大師戴明的傳世經典經營管理的百科全書變革必須從領導者做起，由上往下帶動，才有可能成功。 本書是品管大師戴明（W. Edwards Deming）留給世人的寶藏——關於品質、系統觀、經營管理的知識體系，還有，只用數字來管理員工將會帶來的禍害。 戴明的專長是統計品質管制（SQC，statistical quality control），身為統計學者，他重視「追本溯源」找出真正的問題，而不是「順流而下」只解決表象問題。他透過紅珠實驗（red bead experiment）和漏斗實驗（funnel experiment）提醒管理者，必須注意外力的介入與過度干預所造成的變異（variation）。管理者必須區分變異的發生是出於「共同原因」（common cause）或「特殊原因」（special cause），才能有助於減少變異。戴明認為「變異愈小，品質愈好」，一套能讓員工覺得受到肯定的制度，遠勝於以產量或數字衡量員工的表現，品質會從這個時間點開始，愈來愈好。 戴明提醒管理者必須熟知系統與變異理論，才能了解「只要超乎系統能力的一切作為，都只是徒勞無功」，以免過度迷信表面的數字，或是將未達數字目標的罪過歸咎於員工，卻忽略了潛在的危機。他再三強調，管理者有責任了解問題是出自「系統」還是「個人」；根據研究，一切作業上的問題，85%出自系統內部，而且應該由最高管理者承擔責任，只有15%的問題可歸咎於員工。 雖然戴明從統計的專業，認為凡事都應以數字為證據，不可過度依賴經驗或直覺，不過，他也提醒管理者，最重要的事情往往是無法測量的，設定配額、一日工作量與論件計酬等等的數字標準，可能會帶來禍害。根據他的研究，凡事只講求追求數字的配額制度，有八成的結果會導致效率降低、成本增加，甚至有些人不計手段以達成業績，不但傷害品質也造成公司聲譽受損。最重要的數字往往是那些看不見、無法取得的數字，像是來自滿意顧客的綜效、或是不滿意顧客造成的負面影響，就無法以數字估算。也因此，戴明很反對所謂的目標管理（MBO），反對設定目標然後根據達成率給予獎勵。他認為，按件計酬是人類最退化的手段，看似鼓勵人們增產，卻忽略了品質低落的殺傷力。 戴明說：「必須有勇氣，才能承認自己一直都做錯了某些事，承認自己有待學習，還有更好的方法。」當

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior―a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for un

Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of ef

Includes questions and answers on Exploratory Analysis, Collecting and Producing Data, Probability, and Statistical Inference. Don't just learn why your answer is correct?learn the rationale behind wh

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It c

Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model se

The parameter estimation and hypothesis testing are the basic tools in statistical inference. These technics occur in many applications of data processing. Besides that, methods of Monte Carlo type ha

This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. I

This textbook provides a coherent introduction to the main concepts and methods of one-parameter statistical inference. Intended for students of Mathematics taking their first course in Statistics, th

In a detailed, coherent treatment of the practices of measurement modeling from a Bayesian perspective, this book covers foundational concepts of Bayesian inference and statistical modeling and their

This book delves into the authors’ work toward deeper understanding of statistical inference in terms of reasoning with uncertainty and meaningfulness of probabilistic inferential output. Focusing on

Taking a broad approach, this book explores recent developments in statistics that have impacted actuarial science. It covers inference, Bayesian methods, statistical learning, spatial analysis, and e

Questions and answers on this set of more than 450 flash cards encompass four general statistics-based themes: exploratory analysis, planning a study, probability, and statistical inference. New to th

This book presents a coarse-to-fine framework for learning and inference in large statistical models for natural language processing. The text shows applications of this fast, accurate approach to syn

This introduction to the world of statistics covers exploratory data analysis, methods for collecting data, formal statistical inference, and techniques of regression and analysis of variance. 1983 ed

The Framework for Post-Phylogenetic Systematics reframes biological systematics to reconcile classical and cladistic schools. It combines scientific intuition and statistical inference in a new form o

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book ca

The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by

Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes

Recent Advances in System Reliability discusses developments in modern reliability theory such as signatures, multi-state systems and statistical inference. It describes the latest achievements in the

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a moder

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effectiv

The Dirichlet distribution appears in many areas of application, which include modelling of compositional data, Bayesian analysis, statistical genetics, and nonparametric inference. This book provides

International edition of 9th edition. This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance of theory and methodology. Interes

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each