PROGRESS IN APPLIED STATISTICSRESEARCH -- CONTENTS -- PREFACE -- AN APPROXIMATE FAST BAYESIAN ALGORITHMFOR THE ANALYSIS AND FORECASTING OF THELOGNORMAL TIME SERIES -- Abstract -- 1. Introduction -- 2. The Standard Dynamic Linear Models -- 3. Dynamic Generalized Linear Models -- 4. Conditional Independence Structure -- 5. Lognormal Dynamic Models -- 6. Validation -- 7. Conclusion -- References -- EFFICIENT UNIFORM DESIGNS FOR MIXTUREEXPERIMENTS IN THREE AND FOUR COMPONENTS -- Abstract -- 1 Introduction -- 2 Uniform Designs and Uniformity Measures -- 3 Projection Designs -- 4 Optimality Criteria -- 5 Unconstrained Mixture Experiments -- Definition: -- Method -- 6 Constrained Mixture Experiments -- Example 1: -- Example 2: -- Acknowledgments -- Appendix A -- References -- DESIGN OF ACCELERATED LIFE TESTS FORPERIODIC INSPECTION WITH BURR TYPE IIIDISTRIBUTIONS: MODELS, ASSUMPTIONS ANDAPPLICATIONS -- Abstract -- 1. Introduction -- 2. The Model and Test Method -- Assumptions -- Test Method -- Standardization -- 3. Maximum Likelihood Estimation -- 4. Optimal Test Plans -- Step-I. -- Step-II. -- Sensitivity Analysis -- Sample Size Determination -- 5. Computational Results and Comparative Study -- 6. Test Procedure with Example -- Example -- 7. Conclusion -- References -- PARAMETER ESTIMATION USING CRESSIE-READDIVERGENCE MEASURES WITH EXPONENTIALGROUPED CENSORED DATA -- Abstract -- 1 Introduction -- 2 Computational Results -- 3 Findings and Conclusions -- References -- ESTIMATING THE VARIANCE COMPONENTSOF ACCELERATED DEGRADATION MODELS -- Abstract -- 1 Introduction -- 2 Model and Estimating the Fixed Effect Parameters -- 3 Estimating the Variance Components -- 4 Simulation Study -- 5 Results and Conclusions -- 6 Application -- References -- ON THE RATIO OF THE SYMMETRIC DIFFERENCESOF ORDER STATISTICS -- Abstract -- 1. Introduction -- Lemma 1.1.

2. Main Result -- Lemma 2.1. -- Proof. -- Remark 2.1. -- Theorem 2.1 (asymptotic distribution of the symmetric difference statistic). -- Remark 2.2. -- Proof. -- References -- MEASURING THE SURFACE ROUGHNESS USINGTHE SPATIAL STATISTICS APPLICATION -- Abstract -- 1. Introduction and Notation -- 2. Spatial Statistics Analysis -- 3. Data Analysis -- 4. Conclusion -- References -- DIALLEL CROSSES WITH BLOCK SIZES THREE -- Abstract -- 1. Introduction -- 2. Method of Construction -- 3. Analysis -- Example 3.1. -- Remark 3.1. -- Remark 3.2. -- 4. Complete Diallel Crosses Plan with Unequal Number of Lines -- Definition 4.1. -- 4.1. Method of Construction -- Lemma 4.1. -- 4.2. Analysis -- Example 4.1. -- 5. Partial Diallel Crosses -- Example 5.1. -- Remark 5.1. -- Remark 5.2. -- 6. Conclusion -- Acknowledgements -- Appendix: Tables -- References -- ON CHARACTERIZING DISTRIBUTIONS BYCONDITIONAL EXPECTATIONS OF FUNCTIONS OFGENERALIZED ORDER STATISTICS -- Abstract -- 1. Introduction -- 2. Main Results -- Lemma 1. -- Proof. -- Corollary 1. -- Theorem 1. -- Proof. -- Theorem 2. -- Proof. -- 2.1. Applications -- 3. Characterizations by Reverse Ordering -- Lemma 2. -- Proof. -- Corollary 2. -- Theorem 3. -- Proof. -- Theorem 4. -- Proof. -- 3.1. Applications -- Acknowledgments -- References -- ESTIMATING THE LOCATION AND SCALEPARAMETERS USING RANKED SET SAMPLING -- Abstract -- 1. Introduction -- 2. Estimation Based on a RSS and a MRSS -- 3. Location Family -- 4. Scale Family -- 5. Location-Scale Family -- 6. Calculations -- 7. Application -- 8. Conclusion -- References -- ROBUST ESTIMATION IN CALIBRATIONMODELSUSING THE STUDENT-t DISTRIBUTION -- Abstract -- 1. Introduction -- 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 -- References.

USEFUL RESULTS FOR THE RENEWALAND THE ALTERNATING RENEWAL PROCESS -- Abstract -- 1. Introduction -- 2. Notation -- 3. The Mean Number of Excess Periods in [0, t). -- 4. The Alternating Renewal Process -- The exponential case. -- The exponential case. -- 5. A Correlated Alternating Renewal Process -- 6. The Mean Number of Periods in a Three State Renewal Process -- The exponential case. -- 7. A Correlated Three Stages Renewal Process -- References -- CLASSIFICATION OF MULTIVARIATEREPEATED MEASURES DATA WITHTEMPORAL AUTOCORRELATION -- Abstract -- 1. Introduction -- 2. Classification Rules -- 2.1. Classification Rules with Structured Mean Vectors -- Case 1: -- Classification Rule: -- Maximum Likelihood Estimation of d1,d2,V and S: -- Case 2: -- Classification Rule: -- Case 3: -- Classification Rule: -- Case 4: -- Classification Rule: -- 2.2. Classification Rules with Unstructured Mean Vectors -- Case 1: -- Case 2: -- Case 3: -- Case 4: -- 3. An Example -- 4. A Simulated Study -- References -- BAYESIAN ESTIMATION FOR THE AR(1) MODELUSING ASYMMETRIC LOSS FUNCTIONS -- Abstract -- 1. Introduction -- 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 -- Result 1: -- Result 2: -- 4.2. Alternatives to the Conjugate Prior -- 5. Decision Analysis -- 6. Data Analysis -- References -- BAYESIAN MODELLING FOR RECURRENTLIFETIME DATA WITH A NON HOMOGENEOUSPOISSON PROCESS WITH A FRAILTY TERM WITH AGAMMA OR INVERSE GAUSSIAN DISTRIBUTION -- Abstract -- 1. Introduction -- 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 -- Acknowledgments -- References -- LOCAL INFLUENCE FOR MEASUREMENT ERRORREGRESSION MODELS FOR THE ANALYSIS OFPRETEST/POSTTEST DATA -- Abstract -- 1. Introduction -- 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 -- Acknowledgements -- References -- A TRANSITION MODEL FOR AN ORDEREDCLUSTER OF MIXED CONTINUOUS AND DISCRETERESPONSES WITH NON-MONOTONE MISSINGNESS -- Abstract -- 1. Introduction -- 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 -- References -- ON A NONBINARY S-OPTIMAL DESIGN OVER ACLASS OF MINIMALLY CONNECTED BINARYROW-COLUMN DESIGNS -- Abstract -- 1. Introduction -- 2. Preliminaries -- a. Treatment information matrix -- b. Row information matrix -- c. Column information matrix -- Definition. -- 3. s-optimal Minimal Design -- Lemma -- Theorem -- 4. Concluding Remarks -- Remark 1. -- Remark 2. -- Remark 3. -- References -- THE ERLANGIAN MACHINE INTERFERENCE MODEL:ER/M/2/K/N WITH BALKING, RENEGING ANDHETEROGENEOUS REPAIRMEN -- Abstract -- 1. Introduction -- 2. Analyzing the Problem.

3. The Steady−State Equations and Their Solution -- Example: -- 4. Special Cases -- References -- SOME EXTENSIONS TO DOUBLERANKED SET SAMPLING -- Abstract -- 1. Introduction -- 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 -- Proof: -- 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 -- References -- INDEX.