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eBook Tools for Statistical Inference: Observed Data and Data Augmentation Methods (Lecture Notes in Statistics) download

by Martin A. Tanner

eBook Tools for Statistical Inference: Observed Data and Data Augmentation Methods (Lecture Notes in Statistics) download ISBN: 038797525X
Author: Martin A. Tanner
Publisher: Springer; Softcover reprint of the original 1st ed. 1991 edition (March 30, 1993)
Language: English
Pages: 110
ePub: 1543 kb
Fb2: 1529 kb
Rating: 4.6
Other formats: azw txt docx lit
Category: Math Sciences
Subcategory: Biological Sciences

The observed data methods are applied. The Data Augmentation Algorithm. Lecture Notes in Statistics.

The observed data methods are applied.

Tools for Statistical Inference Observed Data and Data Augmentation Methods. Posted by webmaster at 2:12 pm Tagged with: Statistics. Medical Statistics: For Beginners. Study medical photos.

Tools For Statistical Inference book. Goodreads helps you keep track of books you want to read. Start by marking Tools For Statistical Inference: Observed Data And Data Augmentation Methods as Want to Read: Want to Read savin. ant to Read.

Tools for Statistical Inference - Observed Data and Data Augmentation Methods, Martin A. Tanner (1991). Testing Problems with Linear or Angular Inequality Constraints, Johan C. Akkerboom (1990). Estimation in Semiparametric Models - Some Recent Developments, Johann Pfanzagl (1990).

Explore Further: Topics Discussed in This Paper. Publications referenced by this paper. Showing 1-3 of 3 references. Sampling-based approaches to calculating marginal densities. Alan E. Gelfand, Adrian F. M. Smith.

This book provides a unified introduction to a variety of computational algorithms for Bayesian and likelihood inference. I have added some new examples, as well as included recent results. Exercises have been added at the end of each chapter.

Augmentation of the collected data is used herein t. .Statistical pragmatism embraces all efficient methods in statistical inference. Augmentation of the collected data is used herein to obtain representative population information from a large class of non-representative population's units. Parameter expansion of a probability model is shown to reduce the upper bound on the sum of error probabilities for a test of simple hypotheses, and a measure, R, is proposed for the effect of activating additional component(s) in the sufficient statistic. Do you want to read the rest of this article?

Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution.

Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics

Tools for Statistical Inference : Observed Data and Data Augmentation Methods. The observed data methods are applied directly to the likelihood or posterior density of the observed data.

Tools for Statistical Inference : Observed Data and Data Augmentation Methods.

From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt für Mathematik#
Comments: (2)
Alister
Received the text a week ago! Well written and proofs well worked-out!
Mr_TrOlOlO
Table of Contents:
Preface
I. Introduction
A. Problems
B. Techniques
[References]
II. Observed Data Techniques - Normal Approximation
A. Likelihood/Posterior Density
B. Maximum Likelihood
C. Normal Based Inference
D. The Delta Method
E. Significance Levels
[References]
III. Observed Data Techniques
A. Numerical Integration
B. Laplace Expansion
B1. Moments
B2. Marginalization
C. Monte Carlo Methods
C1. Monte Carlo
C2. Composition
C3. Importance Sampling
[References]
IV. The EM Algorithm
A. Introduction
B. Theory
C. EM in the Exponential Family
D. Standard Errors
D1. Direct Computation
D2. Missing Information Principle
D3. Louis' Method
D4. Simulation
D5. Using EM Iterates
E. Monte Carlo Implementation of the E-Step
F. Acceleration of EM
[References]
V. Data Augmentation
A. Introduction
B. Predictive Distribution
C. HPD Region Computations
C1. Calculating the Content
C2. Calculating the Boundary
D. Implementation
E. Theory
F. Poor Man's Data Augmentation
F1. PMDA #1
F2. PMDA Exact
F3. PMDA #2
G. SIR
H. General Imputation Methods
H1. Introduction
H2. Hot Deck
H3. Simple Residual
H4. Normal and Adjusted Normal
H5. Nonignorable Nonresponse
H5a. Mixture Model-I
H5b. Mixture Model-II
H5c. Selection Model-I
H5d. Selection Model-II
I. Data Augmentation via Importance Sampling
I1. General Comments
I2. Censored Regression
J. Sampling in the Context of Multinomial Data
J1. Dirichlet Sampling
J2. Latent Class Analysis
[References]
VI. The Gibbs Sampler
A. Introduction
A1. Chained Data Augmentation
A2. The Gibbs Sampler
A3. Historical Comments
B. Examples
B1. Rat Growth Data
B2 Poisson Process
B3. Generalized Linear Models
C. The Griddy Gibbs Sampler
C1. Example
C2. Adaptive Grid
[References]
Index