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Quality Management and Operations Research: Understanding and Implementing the Nonparametric Bayesian Approach [Hardback]

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  • Formāts: Hardback, 120 pages, height x width: 234x156 mm, weight: 420 g, 6 Tables, black and white; 15 Line drawings, black and white; 15 Illustrations, black and white
  • Izdošanas datums: 20-Apr-2021
  • Izdevniecība: CRC Press
  • ISBN-10: 0367744902
  • ISBN-13: 9780367744908
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Quality Management and Operations Research: Understanding and Implementing the Nonparametric Bayesian ApproachPalielināt
  • Formāts: Hardback, 120 pages, height x width: 234x156 mm, weight: 420 g, 6 Tables, black and white; 15 Line drawings, black and white; 15 Illustrations, black and white
  • Izdošanas datums: 20-Apr-2021
  • Izdevniecība: CRC Press
  • ISBN-10: 0367744902
  • ISBN-13: 9780367744908
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367744908.html
Keywords:
Offering a step-by-step approach for applying the Nonparametric Method with the Bayesian Approach to model complex relationships occurring in Reliability Engineering, Quality Management, and Operations Research, it also discusses survival and censored data, accelerated lifetime tests (issues in reliability data analysis), and R codes.

This book uses the Nonparametric Bayesian approach in the fields of quality management and operations research. It presents a step-by-step approach for understanding and implementing these models, as well as includes R codes which can be used in any dataset. The book helps the readers to use statistical models in studying complex concepts and applying them to Operations Research, Industrial Engineering, Manufacturing Engineering, Computer Science, Quality and Reliability, Maintenance Planning and Operations Management.

This book helps researchers, analysts, investigators, designers, producers, industrialists, entrepreneurs, and financial market decision makers, with finding the lifetime model of products, and for crucial decision-making in other markets.
Foreword ix
Preface xi
Acknowledgments xv
Authors xvii
Chapter 1 Introduction
1(8)
1.1 Need for Quality
1(1)
1.2 Quality Management
2(2)
1.2.1 Quality Management Parameters
3(1)
1.3 Quality Determinants
4(2)
1.4 Factors Affecting Reliability
6(3)
Chapter 2 Quality and Reliability
9(24)
2.1 Some Remarkable Properties of Survival Data
10(1)
2.2 Important Functions for Assessing Failure Time
10(5)
2.2.1 Cumulative Distribution Function
11(1)
2.2.2 Probability Density Function
11(1)
2.2.3 Survival Function
11(1)
2.2.4 Hazard Function
12(2)
2.2.5 Quantile Function
14(1)
2.3 Censor
15(1)
2.4 Accelerated Lifetime Tests
16(2)
2.4.1 Accelerated Lifetime Tests Models
17(1)
2.4.2 Lifetime-Stress Relationship
18(1)
2.5 Bayesian Approach
18(2)
2.6 Markov Chain Monte Carlo Method
20(11)
2.6.1 Monte Carlo Approach
20(1)
2.6.1.1 Monte Carlo Integration
21(1)
2.6.1.2 Importance Sampling
22(1)
2.6.2 Markov Chain
23(1)
2.6.2.1 Definitions
23(2)
2.6.2.2 Chain Structure
25(1)
2.6.2.3 Limiting Distribution of Chain
25(3)
2.6.3 Metropolis-Hastings Algorithm
28(1)
2.6.3.1 Gibbs Sampling Method
29(1)
2.6.3.2 Some Features of the Gibbs Sampling Method
30(1)
2.7 Slice Sampling
31(2)
Chapter 3 Dirichlet Process
33(26)
3.1 Dirichlet Distribution
33(10)
3.1.1 Remarkable Properties of the Dirichlet Distribution
38(5)
3.2 Dirichlet Process
43(6)
3.3 Polya's Urn Model
49(3)
3.3.1 Polya's Urn Process
49(1)
3.3.2 Blackwell-MacQueen Urn Scheme
50(2)
3.4 Dirichlet Process and Clustering Issue
52(7)
3.4.1 Chinese Restaurant Process
54(5)
Chapter 4 Nonparametric Bayesian Approach in Accelerated Lifetime Tests
59(18)
4.1 Dirichlet Process Mixture Models
60(3)
4.1.1 Mixture Models
60(3)
4.2 Log-linear Regression in the Nonparametric Problem
63(2)
4.3 Determining the Base Distribution and the Precision Parameter
65(1)
4.4 Hierarchical Model of the Dirichlet Process
66(1)
4.5 Bayesian Computation
67(2)
4.6 Model Fitting
69(8)
4.6.1 First Stage: Updating
70(2)
4.6.2 Second Stage: Updating
72(1)
4.6.3 Third Stage: Updating ζ
73(1)
4.6.4 Fourth Stage: Updating β
73(1)
4.6.5 Fifth Stage: Updating the Distribution of Failure-Time
73(4)
Chapter 5 Illustrative Examples and Results
77(20)
5.1 Empirical Distribution Function
77(1)
5.2 Dirichlet Process Weibull Mixture Model
78(2)
5.2.1 Determining Base Distribution
78(2)
5.3 Assessing the Model and Simulation
80(9)
5.3.1 Updating (αi,- λi)
80(3)
5.3.2 Updating (αj, λj)
83(2)
5.3.3 Updating φ, γ, and μ
85(1)
5.3.3.1 Updating φ
85(1)
5.3.3.2 Updating γ
86(1)
5.3.3.3 Updating μ
87(1)
5.3.3.4 Updating β
88(1)
5.4 Illustrative Examples
89(8)
Appendix A Guide to Proofs 97(4)
Appendix B R Programming Codes 101(14)
References 115(4)
Index 119
Nezameddin Faghih is the UNESCO Chair Professor Emeritus, and the Founding Editor-in-Chief of the Journal of Global Entrepreneurship Research (Springer). He has published more than 50 books, 100 research articles, and presented more than 120 invited talks in academia, industry, and professional meetings.

Ebrahim Bonyadi is an applied statistician in the areas of business and economics and is a researcher at the Global Entrepreneurship Monitor (GEM) Office of the Faculty of Entrepreneurship, University of Tehran. His scholarly research focuses on factors influencing entrepreneurship, business, and economic growth.

Lida Sarreshtehdari is an applied statistician focusing on entrepreneurship, with expertise in the Global Entrepreneurship Monitor (GEM) dataset. She is a researcher at the Global Entrepreneurship Monitor (GEM) Office of the Faculty of Entrepreneurship, University of Tehran. She has published several reports on the domestic entrepreneurship since 2011.