【Kinoppy】

「人工知能とデータサイエンス 2019/2020」(電子洋書)ご案内
Part 3.

人工知能とデータサイエンス関連好評書籍をご紹介します。最近一年間の新刊・好評書目の中から、電子書籍でご利用いただけるタイトルをピックアップしてご案内します。

シリーズ最終回の Part 3 は、統計学・確率論編(33点)です。

Part 1.のリストはこちら
Part 2.のリストはこちら

【2019年11月8日更新】

※当ページの出版年は紙の書籍の情報を表示しています。電子書籍の出版年はタイトルクリック後の電子書籍詳細画面をご覧ください。
「紙の書籍はこちら」は、複数の装丁刊行がある場合、その中の一つにリンクされています。紙の書籍詳細画面上に 「装丁違いISBN」のリンクがある場合、こちらから他の装丁刊行情報もご確認いただけます。

1.統計学・確率論

商品詳細へカテゴリカルデータ解析入門(第3版)
An Introduction to Categorical Data Analysis (Wiley Probability and Statistics) 3RD

Agresti, Alan
紙版刊行: 2019/01
Wiley




A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis. New sections in many chapters introducing the Bayesian approach for the methods of that chapter. More than 70 analyses of data sets to illustrate application of the methods, and about 200 excercises, many containing other data sets.

商品詳細へノンパラメトリック統計学へのパラメトリック・アプローチ(テキスト)
A Parametric Approach to Nonparametric Statistics (Springer Series in the Data Sciences)

Alvo, Mayer/ Yu, Philip L.H.
紙版刊行: 2018/10
Springer




This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data.

商品詳細へ統計的学習への計算論的アプローチ
A Computational Approach to Statistical Learning (Chapman & Hall/crc Texts in Statistical Science)

Arnold, Taylor/ Kane, Michael/ Lewis, Bryan W.
紙版刊行: 2019/01
Chapman & Hall




This book synthesizes those techniques from numerical analysis, algorithms, data structures, and optimization theory most commonly employed in statistics and machine learning.

商品詳細へ統計学的意思決定論入門
Introduction to Statistical Decision Theory : Utility Theory and Causal Analysis

Bacci, Silvia/ Chiandotto, Bruno
紙版刊行: 2019/07
Chapman & Hall




It builds from the foundations of decision theory under uncertainty, through both frequentist and Bayesian approaches to statistical decision theory, to the integration of statistical decision theory with causal inference.

商品詳細へ非線形推定:手法と応用
Nonlinear Estimation : Methods and Applications with Deterministic Sample Points

Bhaumik, Shovan/ Date, Paresh
紙版刊行: 2019/07
Chapman & Hall




This book deals with nonlinear state estimation that covers both particle filter and Gaussian filters. The author has provided a complete coverage to the Bayesian estimation with deterministic sample points.

商品詳細へ確率入門(テキスト・第2版)
Introduction to Probability (Chapman & Hall/crc Texts in Statistical Science) 2ND

Blitzstein, Joseph K./ Hwang, Jessica
紙版刊行: 2019/02
Chapman & Hall




This undergraduate probability textbook assumes one-semester of calculus.

商品詳細へテンソル代数統計学入門(テキスト)
An Introduction to Algebraic Statistics with Tensors (UNITEXT)

Bocci, Cristiano/ Chiantini, Luca
紙版刊行: 2019/10
Springer




Includes a self-contained manual of Algebraic Geometry for the study of spaces of tensors.

商品詳細へ応用ベイズ階層モデル:Rによる応用(第2版)
Bayesian Hierarchical Models : With Applications Using R 2 New

Congdon, Peter D.
紙版刊行: 2019/09
Chapman & Hall




This book has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent changes in computing with the emergence of Jags and Stan, both of which will be introduced here. The second edition also features updated throughout, modern references, new examples, etc.

商品詳細へSPSS単変量・二変量・多変量統計データ解析法
SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics

Denis, Daniel J.
紙版刊行: 2018/11
Wiley




This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences.

商品詳細へニュートラルネットワークと統計的学習(テキスト・第2版)
Neural Networks and Statistical Learning 2ND

Du, Ke-lin/ Swamy, M. N. S.
紙版刊行: 2019/10
Springer




Extensively updated second edition with new chapters on spar coding, deep learning, big data and cloud computing.

商品詳細へ確率と統計的推論を用いる未来予測(第2版)
Probably Not : Future Prediction Using Probability and Statistica lInference 2ND

Dworsky, Lawrence N.
紙版刊行: 2019/09
Wiley




Features a new chapter on Benford's Law that explains why we find Benford's law upheld in so many, but not all, natural situations.

商品詳細へベイズ・ネットワークによるリスク評価・意志決定(第2版)
Risk Assessment and Decision Analysis with Bayesian Networks 2ND

Fenton, Norman/ Neil, Martin
紙版刊行: 2018/09
Chapman & Hall




This second edition includes new material on influence dynamics, learning from data, value of information, cybersecurity, debunking bad statistics, and much more.

商品詳細へGARCHモデル:構造、統計的推論と金融への応用(第2版)
GARCH Models : Structure, Statistical Inference and Financial Applications 2ND

Frankq, Christian/ Zakoian, Jean-Michael
紙版刊行: 2019/04
Wiley




The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests.

商品詳細へ長期記憶を視野に入れた時系列分析
Time Series Analysis with Long Memory in View (Wiley Series in Probability and Statistics)

Hassler, Uwe
紙版刊行: 2019/02
Wiley




Presents the general theory of time series, including some issues that are not treated in other books on time series, such as ergodicity, persistence versus memory, asymptotic properties of the periodogram, and Whittle estimation.

商品詳細へRによる統計・データ解析のためのグラフィックス(テキスト・第2版)
Graphics for Statistics and Data Analysis with R (Texts in Statistical Science) 2ND

Keen, Kevin J.
紙版刊行: 2018/05
Chapman & Hall




The second edition will add examples with the R package ggplot2 in addition to examples with the base and lattice packages in the first edition.

商品詳細へ統計的推論:大学院テキスト
A Graduate Course on Statistical Inference (Springer Texts in Statistics)

Li, Bing/ Babu, G. Jogesh
紙版刊行: 2019/08
Springer




This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics.

商品詳細へ十分次元削減:Rによる手法と応用
Sufficient Dimension Reduction : Methods and Applications with R (Monographs on Statistics and Applied Probability)

Li, Bing
紙版刊行: 2018/05
Chapman & Hall




Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data.

商品詳細へ行列微分学と統計学・計量経済学への応用(第3版)
Matrix Differential Calculus with Applications in Statistics and Econometrics (Wiley in Probability and Statistics) 3RD

Magnus, Jan R./ Neudecker, Heinz
紙版刊行: 2019/03
Wiley




データサイエンスの基礎を固めるテキストとしても定評を得てきた行列と微分の包括的テキスト、20年ぶりの全面改訂アップデート版。 A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometrics. This exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique.

商品詳細へRによるロバスト統計学:理論と方法(第2版)
Robust Statistics : Theory and Methods (with R) (Wiley Series in Probability and Statistics) 2ND

Maronna, Ricardo A./ Martin, R. Douglas/ Yohai, Victor J.
紙版刊行: 2018/12
Wiley




Features chapters on estimating location and scale; measuring robustness; linear regression, with fixed and with random predictors; multivariate analysis; genealized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates.

商品詳細へ統計的推論の方法:分野を越える統計学ウォーズを超えるには
Statistical Inference as Severe Testing : How to Get Beyond the Statistics Wars

Mayo, Deborah G.
紙版刊行: 2018/11
Cambridge Univ Pr




The eye-opener illuminate controversies surrounding widely used statistical methods across the physical, social, and biological sciences. New solutions to philosophical problems of induction, falsification, science vs. pseudoscience are put to work to let statisticians and reproducibility researchers get beyond hardened conceptual disagreements.

商品詳細へ大学数学・統計学のためのR(テキスト)
R for College Mathematics and Statistics

Pfaff, Thomas J.
紙版刊行: 2019/04
Chapman & Hall




This book encourages the use of R in the college mathematics classroom. Instructors can require reports and homework with graphs. They can do simulations and experiments.

商品詳細へR応用統計学(テキスト)
Applied Statistics : A Procedure Solution with R

Rasch, Dieter
紙版刊行: 2019/10
Wiley




Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory.

商品詳細へベイズ統計学の手法(テキスト)
Bayesian Statistical Methods (Texts in Statistical Science)

Reich, Brian J./ Ghosh, Sujit K.
紙版刊行: 2019/04
Chapman & Hall




Chapters 1-4 introduce Bayesian concepts at an elementary level. The second part of the book covers advanced topics such as high-dimensional parameter spaces, dependent data models, models for data irregularities and priors on infinite dimensional parameter spaces.

商品詳細へRによる統計計算(テキスト・第2版)
Statistical Computing with R (Chapman & Hall/crc the R) 2 Revised

Rizzo, Maria L.
紙版刊行: 2019/03
Chapman & Hall




初版(2008)邦訳『Rによる計算機統計学』(2011) This second edition continues to encompass the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. This edition also features a new chapter on nonparametric regression and smoothing.

商品詳細へ確率モデル入門(第12版)
Introduction to Probability Models 12TH

Ross, Sheldon M.
紙版刊行: 2019/03
Academic Pr




ロスの1972年以来版を重ねる古典的ベストセラー・テキスト。 This trusted book introduces the reader to elementary probability modelling and stochastic processes and shows how probability theory can be applied in fields such as engineering, computer science, management science, the physical and social sciences and operations research. Prev. ed.: 2014.

商品詳細へRによる時系列データ解析(テキスト)
Time Series : A Data Analysis Approach Using R (Chapman & Hall/crc Texts in Statistical Science)

Shumway, Robert H./ Stoffer, David S.
紙版刊行: 2019/05
Chapman & Hall




This textbook is designed for an introductory time series course where the prerequisites are an understanding of linear regression and some basic probability skills. All of the numerical examples were done using the R statistical package, and the code is typically listed at the end of an example.

商品詳細へ非線形時系列解析
Nonlinear Time Series Analysis (Wiley Series in Probability and Statistics)

Tsay, Ruey S./ Chen, Rong
紙版刊行: 2018/11
Wiley




Dealing with theory and applications, this book considers both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis.

商品詳細へ渡辺澄夫(東京工業大学)著/ベイズ統計学の数理的基盤
Mathematical Theory of Bayesian Statistics

Watanabe, Sumio
紙版刊行: 2018/04
Chapman & Hall




Recently, new research on Bayesian statistics uncovered the mathematical laws by which the behavior of Bayesian inference can be estimated and the advances of Bayes estimation have been clarified. This book introduces such mathematical foundations to students and researchers.

商品詳細へ多変量時系列解析と応用
Multivariate Time Series Analysis and Applications

Wei, William
紙版刊行: 2019/03
Wiley




Beginning with the fundamental concepts and issues of multivariate time series analysis, this book covers many topics that are not found in general multivariate time series books.

2.まずはここから! AI・データサイエンス既刊ベストセラー&ロングセラー

商品詳細へ統計学教育研究国際ハンドブック
International Handbook of Research in Statistics Education (Springer International Handbooks of Education)

Ben-Zvi, Dani/ Makar, Katie/ Garfield, Joan (EDT)
紙版刊行: 2018/01
Springer




「データサイエンス」時代に求められる統計学の教育と研究にたずさわる多数の世界的な専門家を結集して、過去・現在・未来を通して重要な論点を網羅する、包括的なリサーチマップ。不確実性、統計的推論、教師教育、カリキュラム、テクノロジーの利用、学習環境の整備、成績評価など。

商品詳細へ『統計学:Rを用いた入門書 改訂第2版』(原書)
Statistics : An Introduction Using R 2ND

Crawley, Michael J.
紙版刊行: 2014/11
Wiley




邦訳:2016年4月・共立出版 This new edition of a bestselling title offers a concise introduction to a broad array is elementary enough to appeal to a wide range of disciplines.

商品詳細へ統計学・社会科学・生医学のための因果推論入門
Causal Inference for Statistics, Social, and Biomedical Sciences : An Introduction

Imbens, Guido W./ Rubin, Donald B.
紙版刊行: 2015/05
Cambridge Univ Pr




In this groundbreaking text, two world-renowned experts lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. Winner, 2016 PROSE Award for Textbook, Social Sciences.

商品詳細へ統計学的再考:RとStanで学ぶベイズ統計学(テキスト)
Statistical Rethinking : A Bayesian Course with Examples in R and Stan (Chapman & Hall/crc Texts in Statistical Science)

Mcelreath, Richard
紙版刊行: 2015/12
Chapman & Hall




This book provides a more elementary introduction to Bayesian analysis than the Gelman book and is more suitable for graduate students in disciplines other than statistics.