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008 110719s2011 njua sb 001 0 eng d
020 9781118518953|q(e-book)
020 9781118029855|q(hbk.)
040 StDuBDS|beng|cStDuBDS|dUk|dStDuBDSZ|dUkPrAHLS
050 0 QA278.8|b.C45 2011
082 00 310|223
100 1 Chihara, Laura,|d1957-
245 10 Mathematical statistics with resampling and R /|cLaura
Chihara, Tim Hesterberg.
260 Hoboken, N.J. :|bWiley,|cc2011.
300 xiv, 418 p. :|bill.
505 0 Preface xiii 1 Data and Case Studies 1 1.1 Case Study:
Flight Delays 1 1.2 Case Study: Birth Weights of Babies 2
1.3 Case Study: Verizon Repair Times 3 1.4 Sampling 3 1.5
Parameters and Statistics 5 1.6 Case Study: General Social
Survey 5 1.7 Sample Surveys 6 1.8 Case Study: Beer and Hot
Wings 8 1.9 Case Study: Black Spruce Seedlings 8 1.10
Studies 8 1.11 Exercises 10 2 Exploratory Data Analysis 13
2.1 Basic Plots 13 2.2 Numeric Summaries 16 2.2.1 Center
17 2.2.2 Spread 18 2.2.3 Shape 19 2.3 Boxplots 19 2.4
Quantiles and Normal Quantile Plots 20 2.5 Empirical
Cumulative Distribution Functions 24 2.6 Scatter Plots 26
2.7 Skewness and Kurtosis 28 2.8 Exercises 30 3 Hypothesis
Testing 35 3.1 Introduction to Hypothesis Testing 35 3.2
Hypotheses 36 3.3 Permutation Tests 38 3.3.1
Implementation Issues 42 3.3.2 One-Sided and Two-Sided
Tests 47 3.3.3 Other Statistics 48 3.3.4 Assumptions 51
3.4 Contingency Tables 52 3.4.1 Permutation Test for
Independence 54 3.4.2 Chi-Square Reference Distribution 57
3.5 Chi-Square Test of Independence 58 3.6 Test of
Homogeneity 61 3.7 Goodness-of-Fit: All Parameters Known
63 3.8 Goodness-of-Fit: Some Parameters Estimated 66 3.9
Exercises 68 4 Sampling Distributions 77 4.1 Sampling
Distributions 77 4.2 Calculating Sampling Distributions 82
4.3 The Central Limit Theorem 84 4.3.1 CLT for Binomial
Data 87 4.3.2 Continuity Correction for Discrete Random
Variables 89 4.3.3 Accuracy of the Central Limit Theorem
90 4.3.4 CLT for Sampling Without Replacement 91 4.4
Exercises 92 5 The Bootstrap 99 5.1 Introduction to the
Bootstrap 99 5.2 The Plug-In Principle 106 5.2.1
Estimating the Population Distribution 107 5.2.2 How
Useful Is the Bootstrap Distribution? 109 5.3 Bootstrap
Percentile Intervals 113 5.4 Two Sample Bootstrap 114
5.4.1 The Two Independent Populations Assumption 119 5.5
Other Statistics 120 5.6 Bias 122 5.7 Monte Carlo Sampling
: The Second Bootstrap Principle 125 5.8 Accuracy of
Bootstrap Distributions 125 5.8.1 Sample Mean: Large
Sample Size 126 5.8.2 Sample Mean: Small Sample Size 127
5.8.3 Sample Median 127 5.9 How Many Bootstrap Samples are
Needed? 129 5.10 Exercises 129 6 Estimation 135 6.1
Maximum Likelihood Estimation 135 6.1.1 Maximum Likelihood
for Discrete Distributions 136 6.1.2 Maximum Likelihood
for Continuous Distributions 139 6.1.3 Maximum Likelihood
for Multiple Parameters 143 6.2 Method of Moments 146 6.3
Properties of Estimators 148 6.3.1 Unbiasedness 148 6.3.2
Efficiency 151 6.3.3 Mean Square Error 155 6.3.4
Consistency 157 6.3.5 Transformation Invariance 160 6.4
Exercises 161 7 Classical Inference: Confidence Intervals
167 7.1 Confidence Intervals for Means 167 7.1.1
Confidence Intervals for a Mean Known 167 7.1.2 Confidence
Intervals for a Mean Unknown 172 7.1.3 Confidence
Intervals for a Difference in Means 178 7.2 Confidence
Intervals in General 183 7.2.1 Location and Scale
Parameters 186 7.3 One-Sided Confidence Intervals 189 7.4
Confidence Intervals for Proportions 191 7.4.1 The Agresti
Coull Interval for a Proportion 193 7.4.2 Confidence
Interval for the Difference of Proportions 194 7.5
Bootstrap t Confidence Intervals 195 7.5.1 Comparing
Bootstrap t and Formula t Confidence Intervals 200 7.6
Exercises 200 8 Classical Inference: Hypothesis Testing
211 8.1 Hypothesis Tests for Means and Proportions 211
8.1.1 One Population 211 8.1.2 Comparing Two Populations
215 8.2 Type I and Type II Errors 221 8.2.1 Type I Errors
221 8.2.2 Type II Errors and Power 226 8.3 More on Testing
231 8.3.1 On Significance 231 8.3.2 Adjustments for
Multiple Testing 232 8.3.3 P-values Versus Critical
Regions 233 8.4 Likelihood Ratio Tests 234 8.4.1 Simple
Hypotheses and the Neyman Pearson Lemma 234 8.4.2
Generalized Likelihood Ratio Tests 237 8.5 Exercises 239 9
Regression 247 9.1 Covariance 247 9.2 Correlation 251 9.3
Least-Squares Regression 254 9.3.1 Regression Toward the
Mean 258 9.3.2 Variation 259 9.3.3 Diagnostics 261 9.3.4
Multiple Regression 265 9.4 The Simple Linear Model 266
9.4.1 Inference for and 270 9.4.2 Inference for the
Response 273 9.4.3 Comments About Assumptions for the
Linear Model 277 9.5 Resampling Correlation and Regression
279 9.5.1 Permutation Tests 282 9.5.2 Bootstrap Case Study
: Bushmeat 283 9.6 Logistic Regression 286 9.6.1 Inference
for Logistic Regression 291 9.7 Exercises 294 10 Bayesian
Methods 301 10.1 Bayes Theorem 302 10.2 Binomial Data
Discrete Prior Distributions 302 10.3 Binomial Data
Continuous Prior Distributions 309 10.4 Continuous Data
316 10.5 Sequential Data 319 10.6 Exercises 322 11
Additional Topics 327 11.1 Smoothed Bootstrap 327 11.1.1
Kernel Density Estimate 328 11.2 Parametric Bootstrap 331
11.3 The Delta Method 335 11.4 Stratified Sampling 339
11.5 Computational Issues in Bayesian Analysis 340 11.6
Monte Carlo Integration 341 11.7 Importance Sampling 346
11.7.1 Ratio Estimate for Importance Sampling 352 11.7.2
Importance Sampling in Bayesian Applications 355 11.8
Exercises 359 Appendix A Review of Probability 363 A.1
Basic Probability 363 A.2 Mean and Variance 364 A.3 The
Mean of a Sample of Random Variables 366 A.4 The Law of
Averages 367 A.5 The Normal Distribution 368 A.6 Sums of
Normal Random Variables 369 A.7 Higher Moments and the
Moment Generating Function 370 Appendix B Probability
Distributions 373 B.1 The Bernoulli and Binomial
Distributions 373 B.2 The Multinomial Distribution 374 B.3
The Geometric Distribution 376 B.4 The Negative Binomial
Distribution 377 B.5 The Hypergeometric Distribution 378
B.6 The Poisson Distribution 379 B.7 The Uniform
Distribution 381 B.8 The Exponential Distribution 381 B.9
The Gamma Distribution 382 B.10 The Chi-Square
Distribution 385 B.11 The Student s t Distribution 388
B.12 The Beta Distribution 390 B.13 The F Distribution 391
B.14 Exercises 393 Appendix C Distributions Quick
Reference 395 Solutions to Odd-Numbered Exercises 399
Bibliography 407 Index 413
506 1 400 annual accesses.|5UkHlHU
650 0 Resampling (Statistics)
650 0 Statistics.
700 1 Hesterberg, Tim,|d1959-
856 40 |uhttps://www.vlebooks.com/vleweb/product/
openreader?id=Hull&isbn=9781118518953|zGo to ebook
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