Statistical Planning and Inference

Name: Statistical Planning and Inference | Concepts and Applications
Brand: Wiley
Price: 71.99 EUR
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Concepts and Applications

Subir Ghosh(Autor*in)

Wiley (Verlag)

1. Auflage

Erschienen am 29. September 2025

236 Seiten

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Inhalt

Cover
Title Page
Copyright
Contents
Preface
Chapter 1 Foundation of Experiments
1.1 Uncertainties in Evidences
1.2 Examples
1.2.1 The Louis Pasteur Anthrax Vaccination Experiment
1.2.2 The Lanarkshire Milk Experiment: Milk Tests in Lanarkshire Schools
1.3 Replication, Randomization, Blocking, and Blinding
1.3.1 Replication
1.3.2 Randomization
1.3.3 Blocking
1.3.4 Blinding
1.4 Figuring It Out!
Questions and Answers
Bibliography
Bibliography
Chapter 2 Completely Randomized Design
2.1 An Example
2.2 Analyses Using R and SAS
2.3 Figuring It Out!
Bibliography
Chapter 3 Randomized Complete Block Design
3.1 Fixed Effects Model
3.2 Binomial Model for Signs
3.3 Randomization Model
3.4 Mixed Effects Model
3.5 General Mixed Effects Model
3.6 The REML Variance Components Estimates
3.7 BLUEs and BLUPs
3.7.1 The Conditional Model
3.7.2 The Unconditional Model
3.7.3 Computation-The Conditional Model
3.7.4 Computation-The Unconditional Model
3.8 Figuring It Out!
Bibliography
Chapter 4 Randomized Incomplete Block Design
4.1 Model M1: Fixed-Effects Model
4.2 Model M2: Mixed-Effects Model
4.3 Research Questions
4.4 Figuring It Out!
4.5 Definitions
Exercises
Bibliography
Chapter 5 Error Rates
5.1 Definitions of Error Rates
5.2 Single-Stage Methods
5.3 A Multistage Method
5.3.1 Benjamini and Hochberg Method
5.4 Figuring It Out
Questions
Bibliography
Chapter 6 Nutrition Experiment
6.1 Figuring It Out!
Bibliography
Chapter 7 The Pearson Dependence
7.1 Bivariate Normal Distribution
7.2 Estimation of Unknown Parameters
7.2.1 The Unconditional Model
7.2.2 The Conditional Model
7.2.3 Test of Significance
7.3 A Bayesian Estimation
7.4 Exercises
Bibliography
Chapter 8 The Multivariate Dependence
8.1 The Multivariate Normal Distribution
8.2 Inference
8.3 Partial Dependence
8.4 Exercises
Bibliography
Chapter 9 The Conditional Mean Dependence
9.1 LS Estimation
9.2 Ridge Estimation
9.2.1 A Bayesian Estimation
9.3 Dependence of Ridge Estimator on the Tuning Parameter
9.4 LASSO Estimation
9.5 Dependence of LASSO Estimators on the Tuning Parameter
Bibliography
Chapter 10 More Parameters Than Observations
10.1 Learning by Doing-Exercises
Exercises
Bibliography
Chapter 11 Eigenvalues, Eigenvectors, and Applications
11.1 Eigenvalues and Eigenvectors
11.2 Second-Order Response Surface
Exercises
Bibliography
Chapter 12 Covariance Estimation
12.1 Model 1
12.1.1 Characterization of the Covariance Matrix and Its Estimators
12.1.2 Likelihood Function
12.1.3 Properties
12.2 Model 2
12.2.1 Characterization of the Covariance Matrix and Its Estimators
12.3 Model 3
12.4 Model 4
12.5 Model 5
12.6 Exercises
Bibliography
Chapter 13 Discriminant Analysis
13.1 Learning from the Univariate Data-Two Normal Populations with Equal Variances
13.1.1 Discriminant Analysis for the Univariate Data
13.1.2 Example-Univariate Discriminant Analysis
13.2 Learning from the Univariate Data-Two Normal Populations with Unequal Variances
13.2.1 Classification of 25 Versicolor Iris Flowers
13.2.2 Classification of 25 Setosa Iris Flowers
13.2.3 Test of Homogeneity of Variances
13.3 Learning from the Multivariate Data
13.3.1 Classification of Versicolor and Setosa
13.3.2 Classification of Versicolor and Virginica
13.4 Logistic Regression
13.5 Exercises
Bibliography
Chapter 14 Optimizing the Variance-Bias Trade-Off
14.1 Variance-Bias Trade-Off
14.1.1 Example 1
14.1.2 Example 2
14.1.3 Example 3
14.2 Information in Data
14.3 Information and Design in Presence of a Covariate
14.3.1 Information
14.3.2 Optimum Design for a Covariate
14.4 Information and Design in Presence of Multiple Covariates
14.4.1 Information
14.4.2 Exponential Model
14.4.3 Exponential Regression Model with Multiple Covariates
14.4.4 Poisson Log-Linear Model
14.4.5 Non-parametric Regression Model
14.5 Exercises
Bibliography
Chapter 15 Specification, Discrimination, Robustness, and Sensitivity
15.1 The Global and Local Optimal Models
15.2 The T-Optimal Design
15.3 Convex and Concave Functions
15.4 The Kullback-Leibler (KL) Divergence
15.5 The KL Design Optimality
15.6 The Differential Entropy
15.7 Lindley Information Measure
15.8 Joint Entropy, Conditional Entropy, and Mutual Information
15.9 Maximum Entropy Sampling
15.10 Search Linear Models and Search Designs
15.10.1 Factorial Experiments
15.10.2 Search Probability Matrix
15.11 Robustness Against Unavailable Data
15.12 Influential Sets of Observations
15.13 Exercises
Bibliography
Data Index
Subject Index
EULA

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