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[null,"1.","Record Nr.",null,"UNINA9910953949203321"]
[null,null,"Titolo",null,"Progress in applied statistics research / / M. Ahsanullah, editor"]
[null,null,"Pubbl/distr/stampa",null,"New York, : Nova Science Publishers, c2009"]
[null,null,"ISBN",null,"1-61728-664-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (279 p.)"]
[null,null,"Collana",null,"Progress in applied statistics research"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Statistics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- PROGRESS IN APPLIED STATISTICSRESEARCH -- 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∗ -- Abstract -- 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 -- References -- Chapter 3DESIGN 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 -- Sensitivity Analysis -- Sample Size Determination -- 5. Computational Results and Comparative Study -- 6. Test Procedure with Example -- Example -- 7. Conclusion -- References -- Chapter 4PARAMETER ESTIMATION USING CRESSIE-READDIVERGENCE MEASURES WITH EXPONENTIALGROUPED CENSORED DATA∗ -- Abstract -- 1 Introduction -- 2 Computational Results -- 3 Findings and Conclusions -- References -- Chapter 5ESTIMATING THE VARIANCE COMPONENTSOF ACCELERATED DEGRADATION MODELS∗ -- Abstract -- 1 Introduction -- 2 Model and Estimating the Fixed Effect Parameters -- 3 Estimating the Variance "]
[{"$nulls":4},"Components -- 4 Simulation Study -- 5 Results and Conclusions -- 6 Application -- References."]
[{"$nulls":4},"Chapter 6ON THE RATIO OF THE SYMMETRIC DIFFERENCESOF ORDER STATISTICS -- Abstract -- 1. Introduction -- 2. Main Result -- References -- Chapter 7MEASURING THE SURFACE ROUGHNESS USINGTHE SPATIAL STATISTICS APPLICATION -- Abstract -- 1. Introduction and Notation -- 2. Spatial Statistics Analysis -- 3. Data Analysis -- 4. Conclusion -- References -- Chapter 8DIALLEL CROSSES WITH BLOCK SIZES THREE -- Abstract -- 1. Introduction -- 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 -- References -- Chapter 9ON CHARACTERIZING DISTRIBUTIONS BYCONDITIONAL EXPECTATIONS OF FUNCTIONS OFGENERALIZED ORDER STATISTICS -- Abstract -- 1. Introduction -- 2. Main Results -- 2.1. Applications -- 3. Characterizations by Reverse Ordering -- 3.1. Applications -- Acknowledgments -- References -- Chapter 10ESTIMATING THE LOCATION AND SCALEPARAMETERS USING RANKED SET SAMPLING -- Abstract -- 1. Introduction -- 2. Estimation Based on a RSS and a MRSS -- 3. Location Family -- Scale Family -- 5. Location-Scale Family -- 6. Calculations -- 7. Application -- 8. Conclusion -- References -- Chapter 11ROBUST 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 -- Chapter 12USEFUL 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 -- 5. A Correlated Alternating Renewal Process."]
[{"$nulls":4},"6. The Mean Number of Periods in a Three State Renewal Process -- 7. A Correlated Three Stages Renewal Process -- References -- Chapter 13CLASSIFICATION OF MULTIVARIATEREPEATED MEASURES DATA WITHTEMPORAL AUTOCORRELATION -- Abstract -- 1. Introduction -- 2. Classification Rules -- 2.1. Classification Rules with Structured Mean Vectors -- Maximum Likelihood Estimation of d1,d2,V and S: -- Classification Rule: -- Classification Rule: -- Classification Rule: -- 2.2. Classification Rules with Unstructured Mean Vectors -- 3. An Example -- 4. A Simulated Study -- References -- Chapter 14BAYESIAN ESTIMATION FOR THE AR(1) MODELUSING ASYMMETRIC LOSS FUNCTIONS -- Abstract -- 1. Introduction -- 2. Linex Loss Functions -- 3. Rationale Behind the Asymmetric Loss -- 4. Different Prior Models -- 4.1. Conjugate Normal Prior and the Behavior of the Linex Risks -- 4.2. Alternatives to the Conjugate Prior -- 5. Decision Analysis -- 6. Data Analysis -- References -- Chapter 15BAYESIAN MODELLING FOR RECURRENTLIFETIME DATA WITH A NON HOMOGENEOUSPOISSON PROCESS WITH A FRAILTY TERM WITH AGAMMA OR INVERSE GAUSSIAN DISTRIBUTION -- Abstract -- 1. Introduction -- 2. Model Formulation -- 2.1. The Model with a Gamma Frailty Distribution -- 2.2. The Model with an Inverse Gaussian Frailty Distribution -- 3. A Bayesian Approach -- 3.1. The Conditional Posterior for the Model with a Gamma Frailty Distribution -- 3.2. The Conditional Posterior for the Model with a Inverse Gaussian FrailtyDistribution -- 4. Model Selection -- 5. The Animal Carcinogenesis Data -- 6. Estimating the Individual Frailties -- 7. Concluding Remarks -- Acknowledgments -- References -- Chapter 16LOCAL INFLUENCE FOR MEASUREMENT ERRORREGRESSION MODELS FOR THE ANALYSIS OFPRETEST/POSTTEST DATA -- Abstract -- 1. "]
[{"$nulls":4},"Introduction -- 2. Measurement Error Regression Model with Null Intercept."]
[{"$nulls":4},"3. Local Influence Diagnostics -- 3.1. Perturbation of CaseWeights -- 3.2. Perturbation of the Response Variables -- 3.3. Perturbation of the Explanatory Variables -- 3.4. Perturbation of the Variance of the Measurement Errors -- 4. Numerical Illustration -- Appendix A: EM Algorithm -- E Step -- M Step -- Appendix B: Observed Information Matrix -- Acknowledgements -- References -- Chapter 17A TRANSITION MODEL FOR AN ORDEREDCLUSTER OF MIXED CONTINUOUS AND DISCRETERESPONSES WITH NON-MONOTONE MISSINGNESS -- Abstract -- 1. Introduction -- 2. Psychological Disorders Data -- 3. Transition Model for Ordered Cluster or Longitudinal Datawith Non-monotone Missing Responses -- 3.1. Residuals -- 4. A Transition Model for the Psychological Disorders Data -- 4.1. The Model -- 4.2. Likelihood -- 4.3. Results -- 5. Discussion -- Acknowledgment -- References -- Chapter 18ON A NONBINARY S-OPTIMAL DESIGN OVER ACLASS OF MINIMALLY CONNECTED BINARYROW-COLUMN DESIGNS -- Abstract -- 1. Introduction -- 2. Preliminaries -- 3. s-optimal Minimal Design -- 4. Concluding Remarks -- References -- Chapter 19THE ERLANGIAN MACHINE INTERFERENCE MODEL:ER/M/2/K/N WITH BALKING, RENEGING ANDHETEROGENEOUS REPAIRMEN∗ -- Abstract -- 1. Introduction -- 2. Analyzing the Problem -- 3. The Steady−State Equations and Their Solution -- 4. Special Cases -- References -- Chapter 20SOME EXTENSIONS TO DOUBLERANKED SET SAMPLING∗ -- Abstract -- 1. Introduction -- 2. Sampling Methods -- 2.1. Ranked Set Sampling -- 2.2. Median Ranked Set Sampling -- 2.3. Extreme Ranked Set Sampling -- 2.4. Double Ranked Set Sampling -- 2.5. Median Double Ranked Set Sampling -- 2.6. Double Median Ranked Set Sampling -- 2.7. Extreme Double Ranked Set Sampling -- 3. Notations and Some Definitions -- 4. Median Double Ranked Set Sampling -- 4.1. Efficiency of MDRSS -- 4.2. Examples -- 5. Double Median Ranked Set Sampling."]
[{"$nulls":4},"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 -- Blank Page."]
[null,null,null,"Sommario/riassunto","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."]