|
|
|
|
| LEADER |
06486nmm a2200661 u 4500 |
| 001 |
EB001933948 |
| 003 |
EBX01000000000000001096850 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 020 |
|
|
|a 132207822X
|
| 020 |
|
|
|a 1118518950
|
| 020 |
|
|
|a 1118029852
|
| 020 |
|
|
|a 9781118029855
|
| 020 |
|
|
|a 1118625757
|
| 020 |
|
|
|a 9781118518953
|
| 020 |
|
|
|a 9781118625750
|
| 050 |
|
4 |
|a QA278.8
|
| 100 |
1 |
|
|a Chihara, Laura
|
| 245 |
0 |
0 |
|a Mathematical statistics with resampling and R
|c Laura Chihara, Tim Hesterberg
|
| 260 |
|
|
|a Hoboken, N.J.
|b J. Wiley & Sons
|c 2011
|
| 300 |
|
|
|a xiv, 418 pages
|b illustrations
|
| 505 |
0 |
|
|a 7.3 One-Sided Confidence Intervals7.4 Confidence Intervals for Proportions; 7.5 Bootstrap t Confidence Intervals; 7.6 Exercises; Chapter 8: Classical Inference: Hypothesis Testing; 8.1 Hypothesis Tests for Means and Proportions; 8.2 Type I and Type Ii Errors; 8.3 More on Testing; 8.4 Likelihood Ratio Tests; 8.5 Exercises; Chapter 9: Regression; 9.1 Covariance; 9.2 Correlation; 9.3 Least-Squares Regression; 9.4 The Simple Linear Model; 9.5 Resampling Correlation and Regression; 9.6 Logistic Regression; 9.7 Exercises; Chapter 10: Bayesian Methods; 10.1 Bayes'Theorem
|
| 505 |
0 |
|
|a 5.1 Introduction to the Bootstrap5.2 The Plug-in Principle; 5.3 Bootstrap Percentile Intervals; 5.4 Two Sample Bootstrap; 5.5 Other Statistics; 5.6 Bias; 5.7 Monte Carlo Sampling: The "Second Bootstrap Principle"; 5.8 Accuracy of Bootstrap Distributions; 5.9 How Many Bootstrap Samples are Needed?; 5.10 Exercises; Chapter 6: Estimation; 6.1 Maximum Likelihood Estimation; 6.2 Method of Moments; 6.3 Properties of Estimators; 6.4 Exercises; Chapter 7: Classical Inference: Confidence Intervals; 7.1 Confidence Intervals for Means; 7.2 Confidence Intervals in General
|
| 505 |
0 |
|
|a Includes bibliographical references (pages 407-412) and index
|
| 505 |
0 |
|
|a Cover; Half Title page; Title page; Copyright page; Preface; Acknowledgments; Chapter 1: Data and Case Studies; 1.1 Case Study: Flight Delays; 1.2 Case Study: Birth Weights of Babies; 1.3 Case Study: Verizon Repair Times; 1.4 Sampling; 1.5 Parameters and Statistics; 1.6 Case Study: General Social Survey; 1.7 Sample Surveys; 1.8 Case Study: Beer and Hot Wings; 1.9 Case Study: Black Spruce Seedlings; 1.10 Studies; 1.11 Exercises; Chapter 2: Exploratory Data Analysis; 2.1 Basic Plots; 2.2 Numeric Summaries; 2.3 Boxplots; 2.4 Quantiles and Normal Quantile Plots
|
| 505 |
0 |
|
|a 10.2 Binomial Data, Discrete Prior Distributions10.3 Binomial Data, Continuous Prior Distributions; 10.4 Continuous Data; 10.5 Sequential Data; 10.6 Exercises; Chapter 11: Additional Topics; 11.1 Smoothed Bootstrap; 11.2 Parametric Bootstrap; 11.3 The Delta Method; 11.4 Stratified Sampling; 11.5 Computational Issues in Bayesian Analysis; 11.6 Monte Carlo Integration; 11.7 Importance Sampling; 11.8 Exercises; Appendix A: Review of Probability; A.1 Basic Probability; A.2 Mean and Variance; A.3 The Mean of A Sample of Random Variables; A.4 The Law of Averages; A.5 The Normal Distribution
|
| 505 |
0 |
|
|a 2.5 Empirical Cumulative Distribution Functions2.6 Scatter Plots; 2.7 Skewness and Kurtosis; 2.8 Exercises; Chapter 3: Hypothesis Testing; 3.1 Introduction to Hypothesis Testing; 3.2 Hypotheses; 3.3 Permutation Tests; 3.4 Contingency Tables; 3.5 Chi-Square Test of Independence; 3.6 Test of Homogeneity; 3.7 Goodness-of-Fit: All Parameters Known; 3.8 Goodness-of-Fit: Some Parameters Estimated; 3.9 Exercises; Chapter 4: Sampling Distributions; 4.1 Sampling Distributions; 4.2 Calculating Sampling Distributions; 4.3 The Central Limit Theorem; 4.4 Exercises; Chapter 5: The Bootstrap
|
| 653 |
|
|
|a Resampling (Statistics) / fast
|
| 653 |
|
|
|a Rééchantillonnage (Statistique)
|
| 653 |
|
|
|a R (Computer program language) / http://id.loc.gov/authorities/subjects/sh2002004407
|
| 653 |
|
|
|a SOCIAL SCIENCE / Statistics / bisacsh
|
| 653 |
|
|
|a R (Langage de programmation)
|
| 653 |
|
|
|a Statistics / http://id.loc.gov/authorities/subjects/sh85127580
|
| 653 |
|
|
|a R (Computer program language) / fast
|
| 653 |
|
|
|a Resampling (Statistics) / http://id.loc.gov/authorities/subjects/sh92003436
|
| 653 |
|
|
|a Statistics / fast
|
| 653 |
|
|
|a statistics / aat
|
| 653 |
|
|
|a Statistique
|
| 700 |
1 |
|
|a Hesterberg, Tim
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 015 |
|
|
|a GBB192344
|
| 776 |
|
|
|z 9781322078229
|
| 776 |
|
|
|z 132207822X
|
| 776 |
|
|
|z 9781118029855
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781118029855/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 519.5
|
| 082 |
0 |
|
|a 310
|
| 082 |
0 |
|
|a 500
|
| 520 |
|
|
|a Confidence intervals and hypothesis tests
|
| 520 |
|
|
|a Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints
|
| 520 |
|
|
|a The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as
|
| 520 |
|
|
|a Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work
|
| 520 |
|
|
|a Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques
|
| 520 |
|
|
|a Calculation of sampling distributions
|
| 520 |
|
|
|a Maximum likelihood estimation and properties of estimators
|
| 520 |
|
|
|a The Central Limit Theorem
|