The Resource Introduction to Mathematical Statistics, (electronic resource)

Introduction to Mathematical Statistics, (electronic resource)

Label
Introduction to Mathematical Statistics
Title
Introduction to Mathematical Statistics
Creator
Contributor
Provider
Subject
Genre
Language
eng
Cataloging source
IDEBK
http://library.link/vocab/creatorName
Bijma, Fetsje
Index
no index present
LC call number
QA276
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
JSTOR
http://library.link/vocab/subjectName
  • Mathematical statistics
  • MATHEMATICS / Applied
  • MATHEMATICS / Probability & Statistics / General
  • Mathematical statistics
  • EDUCATION / General
Label
Introduction to Mathematical Statistics, (electronic resource)
Instantiates
Publication
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 Region; 4.4. 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 Theory; 6.1. 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-Validation; 8.7. 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 Distribution; C.2. t-Distribution; C.3. Chi-Square Distribution; C.4. Binomial Distribution (n = 10); D. Answers to Exercises; Index
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9789048536115
Media category
computer
Media MARC source
rdamedia
Media type code
c
Specific material designation
remote
Stock number
  • 1024765
  • 22573/ctt1v6c1mz
System control number
  • (OCoLC)999636489
  • (OCoLC)ocn999636489
Label
Introduction to Mathematical Statistics, (electronic resource)
Publication
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 Region; 4.4. 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 Theory; 6.1. 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-Validation; 8.7. 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 Distribution; C.2. t-Distribution; C.3. Chi-Square Distribution; C.4. Binomial Distribution (n = 10); D. Answers to Exercises; Index
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9789048536115
Media category
computer
Media MARC source
rdamedia
Media type code
c
Specific material designation
remote
Stock number
  • 1024765
  • 22573/ctt1v6c1mz
System control number
  • (OCoLC)999636489
  • (OCoLC)ocn999636489

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