Autor/Autorin: Bakhshi Ziaul Hassan
Dr. Ziaul Hassan Bakhshi is working as an Associate Professor in the Department of Management at JIT, Barabanki,India. He received his Ph.D. in Statistics from AMU,Aligarh,India. He had been Assistant Professor in Mekelle University,Ethiopia and SEU,Riyadh,KSA. He has more than 5 yrs teaching experience and 9 research papers pubished to his credit.
This book is based on the application of Stochastic programming techniques in Stratified Sampling problems. Stochastic programming deals with a class of optimization models and algorithms in which some of the data may be considered as random. First chapter provides a brief historical sketch of mathematical programming, stochastic programming and its application to various fields including sampling are presented. Various methods namely E-model, modified E-model, Branch and Bound approach and chance constrained programming are used to solve Probabilistic objective function & constraint into an equivalent deterministic. Allocation problems arising in univariate sratified sampling have been considered in first four (II-V) chapters and multivariate case in the last chapter. The problems are solved by first deriving the deterministic equivalents and then by using a suitable convex programming algorithm or by using the Chance constrained technique. Allocation problem in two stage stratified sampling with random parameters in both objective and constraints is also discussed. A numerical example is presented in all chapters and the problems are solved by LINGO software.
Über den Autor
Dr. Ziaul Hassan Bakhshi is working as an Associate Professor in the Department of Management at JIT, Barabanki,India. He received his Ph.D. in Statistics from AMU,Aligarh,India. He had been Assistant Professor in Mekelle University,Ethiopia and SEU,Riyadh,KSA. He has more than 5 yrs teaching experience and 9 research papers pubished to his credit.
Klappentext
This book is based on the application of Stochastic programming techniques in Stratified Sampling problems. Stochastic programming deals with a class of optimization models and algorithms in which some of the data may be considered as random. First chapter provides a brief historical sketch of mathematical programming, stochastic programming and its application to various fields including sampling are presented. Various methods namely E-model, modified E-model, Branch and Bound approach and chance constrained programming are used to solve Probabilistic objective function & constraint into an equivalent deterministic. Allocation problems arising in univariate sratified sampling have been considered in first four (II-V) chapters and multivariate case in the last chapter. The problems are solved by first deriving the deterministic equivalents and then by using a suitable convex programming algorithm or by using the Chance constrained technique. Allocation problem in two stage stratified sampling with random parameters in both objective and constraints is also discussed. A numerical example is presented in all chapters and the problems are solved by LINGO software.
