Introduction to Mathematical Statistics

Resource Information

The instance Introduction to Mathematical Statistics represents a material embodiment of a distinct intellectual or artistic creation found in University of San Diego Libraries. This resource is a combination of several types including: Instance, Electronic.

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Label

Introduction to Mathematical Statistics

Instantiates

• Introduction to Mathematical Statistics

Publication

• Amsterdam University Press, 2017

Carrier category

online resource

Carrier category code

• cr

Carrier MARC source

rdacarrier

Content category

text

Content type code

• txt

Content type MARC source

rdacontent

Contents

• Cover; Table of Contents; 1. Introduction; 1.1. What Is Statistics?; 1.2. Statistical Models; Exercises; Application: Cox Regression; 2. Descriptive Statistics; 2.1. Introduction; 2.2. Univariate Samples; 2.3. Correlation; 2.4. Summary; Exercises; Application: Benford's Law; 3. Estimators; 3.1. Introduction; 3.2. Mean Square Error; 3.3. Maximum Likelihood Estimators; 3.4. Method of Moments Estimators; 3.5. Bayes Estimators; 3.6. M-Estimators; 3.7. Summary; Exercises; Application: Twin Studies; 4. Hypothesis Testing; 4.1. Introduction; 4.2. Null Hypothesis and Alternative Hypothesis
• 4.3. Sample Size and Critical Region4.4. Testing with p-Values; 4.5. Statistical Significance; 4.6. Some Standard Tests; 4.7. Likelihood Ratio Tests; 4.8. Score and Wald Tests; 4.9. Multiple Testing; 4.10. Summary; Exercises; Application: Shares According to Black-Scholes; 5. Confidence Regions; 5.1. Introduction; 5.2. Interpretation of a Confidence Region; 5.3. Pivots and Near-Pivots; 5.4. Maximum Likelihood Estimators as Near-Pivots; 5.5. Confidence Regions and Tests; 5.6. Likelihood Ratio Regions; 5.7. Bayesian Confidence Regions; 5.8. Summary; Exercises; Application: The Salk Vaccine
• 6. Optimality Theory6.1. Introduction; 6.2. Sufficient Statistics; 6.3. Estimation Theory; 6.4. Testing Theory; 6.5. Summary; Exercises; Application: High Water in Limburg; 7. Regression Models; 7.1. Introduction; 7.2. Linear Regression; 7.3. Analysis of Variance; 7.4. Nonlinear and Nonparametric Regression; 7.5. Classification; 7.6. Cox Regression Model; 7.7. Mixed Models; 7.8. Summary; Exercises; Application: Regression Models and Causality; 8. Model Selection; 8.1. Introduction; 8.2. Goal of Model Selection; 8.3. Test Methods; 8.4. Penalty Methods; 8.5. Bayesian Model Selection
• 8.6. Cross-Validation8.7. Post-Model Selection Analysis; 8.8. Summary; Application: Air Pollution; A. Probability Theory; A.1. Introduction; A.2. Distributions; A.3. Expectation and Variance; A.4. Standard Distributions; A.5. Multivariate and Marginal Distributions; A.6. Independence and Conditioning; A.7. Limit Theorems and the Normal Approximation; Exercises; B. Multivariate Normal Distribution; B.1. Introduction; B.2. Covariance Matrices; B.3. Definition and Basic Properties; B.4. Conditional Distributions; B.5. Multivariate Central Limit Theorem; B.6. Derived Distributions; C. Tables
• C.1. Normal DistributionC. 2. t-Distribution; C.3. Chi-Square Distribution; C.4. Binomial Distribution (n = 10); D. Answersto Exercises; Index

Control code

ocn999636489

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unknown

Extent

1 online resource

Form of item

online

Isbn

9789048536115

Media category

computer

Media MARC source

rdamedia

Media type code

• c

Note

JSTOR

Other control number

10.1515/9789048536115

http://library.link/vocab/ext/overdrive/overdriveId

• 1024765
• 22573/ctt1v6c1mz

Record ID

.b5137030x

Specific material designation

remote

System control number

(OCoLC)999636489