Preface xiii
1 Basic Ideas of Mathematical Statistics 1
1.1 Statistical Population and Samples 2
1.1.1 Concrete Samples and Statistical Populations 2
1.1.2 Sampling Procedures 4
1.2 Mathematical Models for Population and Sample 8
1.3 Sufficiency and Completeness 9
1.4 The Notion of Information in Statistics 20
1.5 Statistical Decision Theory 28
1.6 Exercises 32
References 37
2 Point Estimation 39
2.1 Optimal Unbiased Estimators 41
2.2 Variance-Invariant Estimation 53
2.3 Methods for Construction and Improvement of Estimators 57
2.3.1 Maximum Likelihood Method 57
2.3.2 Least Squares Method 60
2.3.3 Minimum Chi-Squared Method 61
2.3.4 Method of Moments 62
2.3.5 Jackknife Estimators 63
2.3.6 Estimators Based on Order Statistics 64
2.3.6.1 Order and Rank Statistics 64
2.3.6.2 L-Estimators 66
2.3.6.3 M-Estimators 67
2.3.6.4 R-Estimators 68
2.4 Properties of Estimators 68
2.4.1 Small Samples 69
2.4.2 Asymptotic Properties 71
2.5 Exercises 75
References 78
3 Statistical Tests and Confidence Estimations 79
3.1 Basic Ideas of Test Theory 79
3.2 The Neyman–Pearson Lemma 87
3.3 Tests for Composite Alternative Hypotheses and One-Parametric Distribution Families 96
3.3.1 Distributions with Monotone Likelihood Ratio and Uniformly Most Powerful Tests for One-Sided Hypotheses 96
3.3.2 UMPU-Tests for Two-Sided Alternative Hypotheses 105
3.4 Tests for Multi-Parametric Distribution Families 110
3.4.1 General Theory 111
3.4.2 The Two-Sample Problem: Properties of Various Tests and Robustness 124
3.4.2.1 Comparison of Two Expectations 125
3.4.3 Comparison of Two Variances 137
3.4.4 Table for Sample Sizes 138
3.5 Confidence Estimation 139
3.5.1 One-Sided Confidence Intervals in One-Parametric Distribution Families 140
3.5.2 Two-Sided Confidence Intervals in One-Parametric and Confidence Intervals in Multi-Parametric Distribution Families 143
3.5.3 Table for Sample Sizes 146
3.6 Sequential Tests 147
3.6.1 Introduction 147
3.6.2 Wald’s Sequential Likelihood Ratio Test for One-Parametric Exponential Families 149
3.6.3 Test about Mean Values for Unknown Variances 153
3.6.4 Approximate Tests for the Two-Sample Problem 158
3.6.5 Sequential Triangular Tests 160
3.6.6 A Sequential Triangular Test for the Correlation Coefficient 162
3.7 Remarks about Interpretation 169
3.8 Exercises 170
References 176
4 Linear Models – General Theory 179
4.1 Linear Models with Fixed Effects 179
4.1.1 Least Squares Method 180
4.1.2 Maximum Likelihood Method 184
4.1.3 Tests of Hypotheses 185
4.1.4 Construction of Confidence Regions 190
4.1.5 Special Linear Models 191
4.1.6 The Generalised Least Squares Method (GLSM) 198
4.2 Linear Models with Random Effects: Mixed Models 199
4.2.1 Best Linear Unbiased Prediction (BLUP) 200
4.2.2 Estimation of Variance Components 202
4.3 Exercises 203
References 204
5 Analysis of Variance (ANOVA) – Fixed Effects Models (Model I of Analysis of Variance) 207
5.1 Introduction 207
5.2 Analysis of Variance with One Factor (Simple- or One-Way Analysis of Variance) 215
5.2.1 The Model and the Analysis 215
5.2.2 Planning the Size of an Experiment 228
5.2.2.1 General Description for All Sections of This Chapter 228
5.2.2.2 The Experimental Size for the One-Way Classification 231
5.3 Two-Way Analysis of Variance 232
5.3.1 Cross-Classification (A × B) 233
5.3.1.1 Parameter Estimation 236
5.3.1.2 Testing Hypotheses 244
5.3.2 Nested Classification (A B) 260
5.4 Three-Way Classification 272
5.4.1 Complete Cross-Classification (A × B × C) 272
5.4.2 Nested Classification (C ?B?A) 279
5.4.3 Mixed Classification 282
5.4.3.1 Cross-Classification between Two Factors Where One of Them Is Subordinated to a Third Factor B?a × c 282
5.4.3.2 Cross-Classification of Two Factors in Which a Third Factor Is Nested C ? A × B 288
5.5 Exercises 291
References 291
6 Analysis of Variance: Estimation of Variance Components (Model II of the Analysis of Variance) 293
6.1 Introduction: Linear Models with Random Effects 293
6.2 One-Way Classification 297
6.2.1 Estimation of Variance Components 300
6.2.1.1 Analysis of Variance Method 300
6.2.1.2 Estimators in Case of Normally Distributed Y 302
6.2.1.3 REML Estimation 304
6.2.1.4 Matrix Norm Minimising Quadratic Estimation 305
6.2.1.5 Comparison of Several Estimators 306
6.2.2 Tests of Hypotheses and Confidence Intervals 308
6.2.3 Variances and Properties of the Estimators of the Variance Components 310
6.3 Estimators of Variance Components in the Two-Way and Three-Way Classification 315
6.3.1 General Description for Equal and Unequal Subclass Numbers 315
6.3.2 Two-Way Cross-Classification 319
6.3.3 Two-Way Nested Classification 324
6.3.4 Three-Way Cross-Classification with Equal Subclass Numbers 326
6.3.5 Three-Way Nested Classification 333
6.3.6 Three-Way Mixed Classification 335
6.4 Planning Experiments 336
6.5 Exercises 338
References 339
7 Analysis of Variance – Models with Finite Level Populations and Mixed Models 341
7.1 Introduction: Models with Finite Level Populations 341
7.2 Rules for the Derivation of SS, df, MS and E(MS) in Balanced ANOVA Models 343
7.3 Variance Component Estimators in Mixed Models 348
7.3.1 An Example for the Balanced Case 349
7.3.2 The Unbalanced Case 351
7.4 Tests for Fixed Effects and Variance Components 353
7.5 Variance Component Estimation and Tests of Hypotheses in Special Mixed Models 354
7.5.1 Two-Way Cross-Classification 355
7.5.2 Two-Way Nested Classification B ? A 358
7.5.2.1 Levels of A Random 360
7.5.2.2 Levels of B Random 361
7.5.3 Three-Way Cross-Classification 362
7.5.4 Three-Way Nested Classification 365
7.5.5 Three-Way Mixed Classification 368
7.5.5.1 The Type (B ? A)×C 368
7.5.5.2 The Type C ? AB 371
7.6 Exercises 374
References 374
8 Regression Analysis – Linear Models with Non-random Regressors (Model I of Regression Analysis) and with Random Regressors (Model II of Regression Analysis) 377
8.1 Introduction 377
8.2 Parameter Estimation 380
8.2.1 Least Squares Method 380
8.2.2 Optimal Experimental Design 394
8.3 Testing Hypotheses 397
8.4 Confidence Regions 406
8.5 Models with Random Regressors 410
8.5.1 Analysis 410
8.5.2 Experimental Designs 415
8.6 Mixed Models 416
8.7 Concluding Remarks about Models of Regression Analysis 417
8.8 Exercises 419
References 419
9 Regression Analysis – Intrinsically Non-linear Model I 421
9.1 Estimating by the Least Squares Method 424
9.1.1 Gauss–Newton Method 425
9.1.2 Internal Regression 431
9.1.3 Determining Initial Values for Iteration Methods 433
9.2 Geometrical Properties 434
9.2.1 Expectation Surface and Tangent Plane 434
9.2.2 Curvature Measures 440
9.3 Asymptotic Properties and the Bias of LS Estimators 443
9.4 Confidence Estimations and Tests 447
9.4.1 Introduction 447
9.4.2 Tests and Confidence Estimations Based on the Asymptotic Covariance Matrix 451
9.4.3 Simulation Experiments to Check Asymptotic Tests and Confidence Estimations 452
9.5 Optimal Experimental Design 454
9.6 Special Regression Functions 458
9.6.1 Exponential Regression 458
9.6.1.1 Point Estimator 458
9.6.1.2 Confidence Estimations and Tests 460
9.6.1.3 Results of Simulation Experiments 463
9.6.1.4 Experimental Designs 466
9.6.2 The Bertalanffy Function 468
9.6.3 The Logistic (Three-Parametric Hyperbolic Tangent) Function 473
9.6.4 The Gompertz Function 476
9.6.5 The Hyperbolic Tangent Function with Four Parameters 480
9.6.6 The Arc Tangent Function with Four Parameters 484
9.6.7 The Richards Function 487
9.6.8 Summarising the Results of Sections 9.6.1–9.6.7 487
9.6.9 Problems of Model Choice 488
9.7 Exercises 489
References 490
10 Analysis of Covariance (ANCOVA) 495
10.1 Introduction 495
10.2 General Model I–I of the Analysis of Covariance 496
10.3 Special Models of the Analysis of Covariance for the Simple Classification 503
10.3.1 One Covariable with Constant γ 504
10.3.2 A Covariable with Regression Coefficients Γ I Depending on the Levels of the Classification Factor 506
10.3.3 A Numerical Example 507
10.4 Exercises 510
References 511
11 Multiple Decision Problems 513
11.1 Selection Procedures 514
11.1.1 Basic Ideas 514
11.1.2 Indifference Zone Formulation for Expectations 516
11.1.2.1 Selection of Populations with Normal Distribution 517
11.1.2.2 Approximate Solutions for Non-normal Distributions and t =1 529
11.1.3 Selection of a Subset Containing the Best Population with Given Probability 531
11.1.3.1 Selection of the Normal Distribution with the Largest Expectation 534
11.1.3.2 Selection of the Normal Distribution with Smallest Variance 534
11.2 Multiple Comparisons 539
11.2.1 Confidence Intervals for All Contrasts: Scheffe’s Method 542
11.2.2 Confidence Intervals for Given Contrasts: Bonferroni’s and Dunn’s Method 548
11.2.3 Confidence Intervals for All Contrasts for N I = N: Tukey’s Method 550
11.2.4 Confidence Intervals for All Contrasts: Generalised Tukey’s Method 553
11.2.5 Confidence Intervals for the Differences of Treatments with a Control: Dunnett’s Method 554
11.2.6 Multiple Comparisons and Confidence Intervals 556
11.2.7 Which Multiple Comparison Shall Be Used? 559
11.3 A Numerical Example 559
11.4 Exercises 563
References 563
12 Experimental Designs 567
12.1 Introduction 568
12.2 Block Designs 571
12.2.1 Completely Balanced Incomplete Block Designs (BIBD) 574
12.2.2 Construction Methods of BIBD 582
12.2.3 Partially Balanced Incomplete Block Designs 596
12.3 Row–Column Designs 600
12.4 Factorial Designs 603
12.5 Programs for Construction of Experimental Designs 604
12.6 Exercises 604
References 605
Appendix A: Symbolism 609
Appendix B: Abbreviations 611
Appendix C: Probability and Density Functions 613
Appendix D: Tables 615
Solutions and Hints for Exercises 627
Index 659
