Progress in applied statistics research / M. Ahsanullah, editor.

Format:

Book

Contributor:

Ahsanullah, Mohammad.

Series:

Progress in applied statistics research

Language:

English

Subjects (All):

Statistics.

Physical Description:

1 online resource (279 p.)

Edition:

1st ed.

Place of Publication:

New York : Nova Science Publishers, c2009.

Language Note:

English

Summary:

Computers have taken a permanent place in almost every human endeavor in the last 20 years. This infiltration requires a learning process on the part of the people utilizing them and realizing where and how they can be best used beyond the basic and obvious applications. Statistics is an example of their application in many diverse fields to reach conclusions and make projections never before possible. Beyond this, applied statistics is rapidly becoming not only an instrument, but an integral part of the advance of knowledge. There are many fields such as medicine, biology, weather prediction, military planning, and many others where the statistical studies are essential before the next step can be taken. This new book presents the latest research in the field.

Contents:

Intro

PROGRESS IN APPLIED STATISTICSRESEARCH

CONTENTS

PREFACE

Chapter 1AN APPROXIMATE FAST BAYESIAN ALGORITHMFOR THE ANALYSIS AND FORECASTING OF THELOGNORMAL TIME SERIES

Abstract

1. Introduction

2. The Standard Dynamic Linear Models

The observation equation

The state equation

3. Dynamic Generalized Linear Models

4. Conditional Independence Structure

5. Lognormal Dynamic Models

6. Validation

7. Conclusion

References

Chapter 2EFFICIENT UNIFORM DESIGNS FOR MIXTUREEXPERIMENTS IN THREE AND FOUR COMPONENTS∗

1 Introduction

2 Uniform Designs and Uniformity Measures

3 Projection Designs

4 Optimality Criteria

5 Unconstrained Mixture Experiments

6 Constrained Mixture Experiments

Acknowledgments

Appendix A

Chapter 3DESIGN OF ACCELERATED LIFE TESTS FORPERIODIC INSPECTION WITH BURR TYPE IIIDISTRIBUTIONS: MODELS, ASSUMPTIONS ANDAPPLICATIONS

2. The Model and Test Method

Assumptions

Test Method

Standardization

3. Maximum Likelihood Estimation

4. Optimal Test Plans

Sensitivity Analysis

Sample Size Determination

5. Computational Results and Comparative Study

6. Test Procedure with Example

Example

Chapter 4PARAMETER ESTIMATION USING CRESSIE-READDIVERGENCE MEASURES WITH EXPONENTIALGROUPED CENSORED DATA∗

2 Computational Results

3 Findings and Conclusions

Chapter 5ESTIMATING THE VARIANCE COMPONENTSOF ACCELERATED DEGRADATION MODELS∗

2 Model and Estimating the Fixed Effect Parameters

3 Estimating the Variance Components

4 Simulation Study

5 Results and Conclusions

6 Application

References.

Chapter 6ON THE RATIO OF THE SYMMETRIC DIFFERENCESOF ORDER STATISTICS

2. Main Result

Chapter 7MEASURING THE SURFACE ROUGHNESS USINGTHE SPATIAL STATISTICS APPLICATION

1. Introduction and Notation

2. Spatial Statistics Analysis

3. Data Analysis

4. Conclusion

Chapter 8DIALLEL CROSSES WITH BLOCK SIZES THREE

2. Method of Construction

3. Analysis

4. Complete Diallel Crosses Plan with Unequal Number of Lines

4.1. Method of Construction

4.2. Analysis

5. Partial Diallel Crosses

6. Conclusion

Acknowledgements

Appendix: Tables

Chapter 9ON CHARACTERIZING DISTRIBUTIONS BYCONDITIONAL EXPECTATIONS OF FUNCTIONS OFGENERALIZED ORDER STATISTICS

2. Main Results

2.1. Applications

3. Characterizations by Reverse Ordering

3.1. Applications

Chapter 10ESTIMATING THE LOCATION AND SCALEPARAMETERS USING RANKED SET SAMPLING

2. Estimation Based on a RSS and a MRSS

3. Location Family

Scale Family

5. Location-Scale Family

6. Calculations

7. Application

8. Conclusion

Chapter 11ROBUST ESTIMATION IN CALIBRATIONMODELSUSING THE STUDENT-t DISTRIBUTION

2. The Calibration Model without Measurement Error

2.1. A Simulation Study

2.2. Application

3. The Functional Calibration Model

3.1. A Simulation Study

Chapter 12USEFUL RESULTS FOR THE RENEWALAND THE ALTERNATING RENEWAL PROCESS

2. Notation

3. The Mean Number of Excess Periods in [0, t).

4. The Alternating Renewal Process

5. A Correlated Alternating Renewal Process.

6. The Mean Number of Periods in a Three State Renewal Process

7. A Correlated Three Stages Renewal Process

Chapter 13CLASSIFICATION OF MULTIVARIATEREPEATED MEASURES DATA WITHTEMPORAL AUTOCORRELATION

2. Classification Rules

2.1. Classification Rules with Structured Mean Vectors

Maximum Likelihood Estimation of d1,d2,V and S:

Classification Rule:

2.2. Classification Rules with Unstructured Mean Vectors

3. An Example

4. A Simulated Study

Chapter 14BAYESIAN ESTIMATION FOR THE AR(1) MODELUSING ASYMMETRIC LOSS FUNCTIONS

2. Linex Loss Functions

3. Rationale Behind the Asymmetric Loss

4. Different Prior Models

4.1. Conjugate Normal Prior and the Behavior of the Linex Risks

4.2. Alternatives to the Conjugate Prior

5. Decision Analysis

6. Data Analysis

Chapter 15BAYESIAN MODELLING FOR RECURRENTLIFETIME DATA WITH A NON HOMOGENEOUSPOISSON PROCESS WITH A FRAILTY TERM WITH AGAMMA OR INVERSE GAUSSIAN DISTRIBUTION

2. Model Formulation

2.1. The Model with a Gamma Frailty Distribution

2.2. The Model with an Inverse Gaussian Frailty Distribution

3. A Bayesian Approach

3.1. The Conditional Posterior for the Model with a Gamma Frailty Distribution

3.2. The Conditional Posterior for the Model with a Inverse Gaussian FrailtyDistribution

4. Model Selection

5. The Animal Carcinogenesis Data

6. Estimating the Individual Frailties

7. Concluding Remarks

Chapter 16LOCAL INFLUENCE FOR MEASUREMENT ERRORREGRESSION MODELS FOR THE ANALYSIS OFPRETEST/POSTTEST DATA

2. Measurement Error Regression Model with Null Intercept.

3. Local Influence Diagnostics

3.1. Perturbation of CaseWeights

3.2. Perturbation of the Response Variables

3.3. Perturbation of the Explanatory Variables

3.4. Perturbation of the Variance of the Measurement Errors

4. Numerical Illustration

Appendix A: EM Algorithm

E Step

M Step

Appendix B: Observed Information Matrix

Chapter 17A TRANSITION MODEL FOR AN ORDEREDCLUSTER OF MIXED CONTINUOUS AND DISCRETERESPONSES WITH NON-MONOTONE MISSINGNESS

2. Psychological Disorders Data

3. Transition Model for Ordered Cluster or Longitudinal Datawith Non-monotone Missing Responses

3.1. Residuals

4. A Transition Model for the Psychological Disorders Data

4.1. The Model

4.2. Likelihood

4.3. Results

5. Discussion

Acknowledgment

Chapter 18ON A NONBINARY S-OPTIMAL DESIGN OVER ACLASS OF MINIMALLY CONNECTED BINARYROW-COLUMN DESIGNS

2. Preliminaries

3. s-optimal Minimal Design

4. Concluding Remarks

Chapter 19THE ERLANGIAN MACHINE INTERFERENCE MODEL:ER/M/2/K/N WITH BALKING, RENEGING ANDHETEROGENEOUS REPAIRMEN∗

2. Analyzing the Problem

3. The Steady−State Equations and Their Solution

4. Special Cases

Chapter 20SOME EXTENSIONS TO DOUBLERANKED SET SAMPLING∗

2. Sampling Methods

2.1. Ranked Set Sampling

2.2. Median Ranked Set Sampling

2.3. Extreme Ranked Set Sampling

2.4. Double Ranked Set Sampling

2.5. Median Double Ranked Set Sampling

2.6. Double Median Ranked Set Sampling

2.7. Extreme Double Ranked Set Sampling

3. Notations and Some Definitions

4. Median Double Ranked Set Sampling

4.1. Efficiency of MDRSS

4.2. Examples

5. Double Median Ranked Set Sampling.

5.1. Efficiency of DMRSS

5.2. Examples

6. Extreme Double Ranked Set Sampling

6.1. Efficiency of EDRSS

6.2. Examples

7. Results and Discussion

Appendix

INDEX

Blank Page.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-61728-664-8

OCLC:

662453158