LEADER 09320nam 2200505 450 001 996472038403316 005 20221118124559.0 010 $a981-16-7932-0 035 $a(MiAaPQ)EBC6953194 035 $a(Au-PeEL)EBL6953194 035 $a(CKB)21511118600041 035 $a(PPN)262169363 035 $a(EXLCZ)9921511118600041 100 $a20221118d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApplied statistical methods $eISGES 2020, Pune, India, January 2-4 /$fedited by David D. Hanagal, Raosaheb V. Latpate, and Girish Chandra 210 1$aGateway East, Singapore :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (318 pages) 225 1 $aSpringer Proceedings in Mathematics and Statistics Ser. ;$vv.380 311 08$aPrint version: Hanagal, David D. Applied Statistical Methods Singapore : Springer Singapore Pte. Limited,c2022 9789811679315 320 $aIncludes bibliographical references. 327 $aIntro -- Preface -- Acknowledgments -- Obituary -- Contents -- Editors and Contributors -- Bayesian Order-Restricted Inference of Multinomial Counts from Small Areas -- 1 Introduction -- 2 Multinomial Dirichlet Models -- 2.1 Model Without Order Restriction (M1) -- 2.2 Model with Order Restrictions (M2) -- 3 Computations -- 3.1 Sampling ? in M2 -- 3.2 Gibbs Sampling for µ and ? -- 4 Application to BMI -- 4.1 Body Mass Index -- 4.2 MCMC Convergence -- 4.3 Model Comparison -- 5 Bayesian Diagnostics -- 6 Conclusion -- 7 Appendix -- 7.1 Details of Gibbs Sampling for µ and ? -- 7.2 Model Comparison -- References -- A Hierarchical Bayesian Beta-Binomial Model for Sub-areas -- 1 Introduction -- 2 Hierarchical Bayesian Small Area Models -- 2.1 A One-Fold Beta-Binomial Model -- 2.2 A Two-Fold Beta-Binomial Model -- 3 Computation -- 3.1 Approximation Method -- 3.2 Exact Method -- 4 Numerical Example -- 4.1 Nepal Living Standards Survey II -- 4.2 Numerical Comparison -- 5 Conclusion and Future Work -- Appendix A Some Details about Approximation of ?(µi |?) -- Appendix B Propriety of the One-Fold Model -- References -- Hierarchical Bayes Inference from Survey-Weighted Small Domain Proportions -- 1 Introduction -- 2 Hierarchical Bayesian Framework -- 3 Application -- 4 Concluding Remarks -- References -- Efficiency of Ranked Set Sampling Design in Goodness of Fit Tests for Cauchy Distribution -- 1 Introduction -- 2 Goodness of Fit Test for Cauchy Distribution -- 3 Power Comparison -- 4 Conclusion -- References -- Fuzzy Supply Chain Newsboy Problem Under Lognormal Distributed Demand for Bakery Products -- 1 Introduction -- 2 Preliminary Definitions and Mathematical Model -- 2.1 Notations and Assumptions -- 2.2 Mathematical Model -- 3 Numerical Case Study -- 4 Results and Discussion -- 5 Conclusions -- References. 327 $aProbabilistic Supply Chain Models with Partial Backlogging for Deteriorating Items -- 1 Introduction -- 2 Mathematical Model -- 2.1 Preliminaries -- 3 Genetic Algorithm -- 4 Numerical Example -- 5 Sensitivity Analysis -- 6 Managerial Implications -- 7 Conclusions -- References -- The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction -- 1 Introduction -- 2 Evolution of Dynamic Gaussian Process Model -- 2.1 Basic Gaussian Process Model -- 2.2 Dynamic Gaussian Process Model -- 2.3 Generalizations for Big Data -- 3 Application: Malaria Vaccination Coverage -- 4 Concluding Remarks -- References -- Grey Relational Analysis for the Selection of Potential Isolates of Alternaria Alternata of Poplar -- 1 Introduction -- 2 Material and Methods -- 2.1 Survey and Collection of Alternaria Isolates -- 2.2 GRA for Selection of Potent Isolates -- 3 Results -- 3.1 Growth Attributes of A. Alternata -- 3.2 Grey Relational Generating, Coefficients and Grades -- 3.3 Performance Evaluation of Selected Isolates -- 4 Discussion and Conclusion -- References -- Decision Making for Multi-Items Inventory Models -- 1 Introduction -- 2 Notations and Assumptions -- 2.1 Assumptions -- 2.2 Notations -- 3 Mathematical Model -- 4 Numerical Example and Comparison Study -- 5 Conclusion -- References -- Modeling Australian Twin Data Using Generalized Lindley Shared Frailty Models -- 1 Introduction -- 2 Reversed Hazard Rate -- 3 General Shared Frailty Model -- 4 Generalized Lindley Frailty Model -- 5 Dependence Measure -- 6 Baseline Distributions -- 6.1 Modified Inverse Weibull Distribution -- 6.2 Generalized Rayleigh Distribution -- 7 Proposed Models -- 8 Statistical Properties -- 8.1 Bivariate Density Function -- 8.2 Bivariate Survival Function -- 8.3 Hazard Gradient Function -- 8.4 Conditional Probability Measure. 327 $a8.5 Cross-ratio Function -- 9 Likelihood Design and Bayesian Paradigm -- 10 Simulation Study -- 11 Analysis of Australian Twin Data -- 12 Conclusions -- References -- Ultimate Ruin Probability for Benktander Gibrat Risk Model -- 1 Introduction -- 2 Risk Model -- 3 Laplace Transformation -- 4 Ultimate Ruin Probability for BG Distribution -- 5 Calculation of Ultimate Ruin Probability -- 6 Conclusions -- References -- Test of Homogeneity of Scale Parameters Based on Function of Sample Quasi Ranges -- 1 Introduction -- 2 Proposed Test Procedure -- 3 Calculation of Critical Points for Some Specific Distributions: Simulation Method -- 3.1 Critical Points for Standard Exponential, Standard Logistic and Standard Uniform Distributions -- 4 Simultaneous One-Sided Confidence Intervals (SOCI's) of the Proposed Test -- 4.1 Simulated Example to Compute Test Statistic and SOCIs -- 5 Power of the Proposed Test -- 6 Conclusion -- References -- A Bayesian Response-Adaptive, Covariate-Balanced and Q-Learning-Decision-Consistent Randomization Method for SMART Designs -- 1 Introduction -- 2 Methods -- 2.1 Overview of the SMART Design -- 2.2 Randomization Probability Using Q-Learning-Based Optimal Decisions -- 2.3 Covariate-Balanced Randomization Probability According to the Prognostic Score for SMART Designs -- 2.4 Response-Adaptive Randomization Probability Based on Outcomes of Previous Groups -- 2.5 Response-Adaptive, Covariate-Balanced and Q-Learning-Decision-Consistent (RCQ) Randomization Method -- 3 Simulations Models and Assessment Measures -- 3.1 Simulation Models -- 3.2 Assessment Measures -- 3.3 Simulation Results -- 4 Discussion -- References -- An Introduction to Bayesian Inference for Finite Population Characteristics -- 1 Introduction -- 2 Normal Distribution -- 3 Regression -- 4 Dirichlet Process -- 5 Multiple Regression with Post-stratification. 327 $a6 Categorical Data -- 7 Summary and Discussion -- References -- Reliability Measures of Repairable Systems with Arrival Time of Server -- 1 Introduction -- 2 Literature Review -- 3 Some Fundamentals -- 3.1 Reliability -- 3.2 Mean Time to System Failure (MTSF) -- 3.3 Steady-State Availability -- 3.4 Redundancy -- 3.5 Semi-Markov Process -- 3.6 Regenerative Point Process -- 4 Common Notations -- 5 Reliability Measures of Repairable Systems -- 5.1 MTSF and Availability of a Single Unit System with Arrival Time of the Server -- 5.2 MTSF and Availability of a Two-Unit Cold Standby System with Arrival Time of the Server -- 5.3 MTSF and Availability of a Two-Unit Parallel System with Arrival Time of the Server -- 6 Discussion and Conclusion -- References -- Stress-strength Reliability Estimation for Multi-component System Based on Upper Record Values Under New Weibull-Pareto Distribution -- 1 Introduction -- 2 System Reliability -- 3 Maximum Likelihood Estimators (MLE) of Parameters -- 4 Likelihood Ratio (LR) Test for Equality of Scale Parameters -- 5 Estimation of Rs,k Using Maximum Likelihood and Bayesian Methods -- 6 Simulation Study -- 7 Real Data Analysis -- 8 Summary and Conclusions -- References -- Record Values and Associated Inference on Muth Distribution -- 1 Introduction -- 2 Survival Function, Joint and Conditional Densities, and Moments of Upper Records from Muth Distribution -- 3 Parameter Estimation Based on Upper Records Using Moment, Likelihood, and Bayesian Approaches -- 3.1 Moment Estimation of ? -- 3.2 Maximum Likelihood Estimation -- 3.3 Bayesian Estimation -- 4 Numerical Illustration -- 5 Real-life Application -- 6 Prediction of Future Records -- 6.1 Frequentist Approach -- 6.2 Bayesian Approach -- 7 Concluding Remarks -- References -- Statistical Linear Calibration in Data with Measurement Errors -- 1 Introduction. 327 $a2 Development of Calibration Estimators -- 3 Performance Properties -- 3.1 Large Sample Asymptotic Bias (LSAB) -- 3.2 Large Sample Asymptotic Variance (LSAV) -- 4 An Example -- 5 Conclusions -- References. 410 0$aSpringer Proceedings in Mathematics and Statistics Ser. 606 $aMathematical statistics 615 0$aMathematical statistics. 676 $a519.54 702 $aHanagal$b David D. 702 $aLatpate$b Raosaheb V. 702 $aChandra$b Girish 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996472038403316 996 $aApplied statistical methods$92968871 997 $aUNISA