Bikas Kumar Sinha – författare

There has been an enormous growth in recent years in the literature on discrete optimal designs. The optimality problems have been formulated in various models arising in the experimental designs and substantial progress has been made towards solving some of these. The subject has now reached a stage of completeness which calls for a self-contained monograph on this topic. The aim of this monograph is to present the state of the art and to focus on more recent advances in this rapidly developing area. We start with a discussion of statistical optimality criteria in Chapter One. Chapters Two and Three deal with optimal block designs. Row-column designs are dealt with in Chapter Four. In Chapter Five we deal with optimal designs with mixed effects models. Repeated measurement designs are considered in Chapter Six. Chapter Seven deals with some special situations and Weighing designs are dis­ cussed in Chapter Eight. We have endeavoured to include all the major developments that have taken place in the last three decades. The book should be of use to research workers in several areas including combinatorics as well as to the experimenters in diverse fields of applications. Since the details of the construction of the designs are available in excellent books, we have only pointed out the designs which have optimality proper­ ties. We believe, this will be adequate for the experimenters.

This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a suitable choice of designs is of great importance; as such choices allow experimenters to extract maximum information for the unknown model parameters. The main emphasis of this monograph is to start with an assumed covariate model in combination with some standard ANOVA set-ups such as CRD, RBD, BIBD, GDD, BTIBD, BPEBD, cross-over, multi-factor, split-plot and strip-plot designs, treatment control designs, etc. and discuss the nature and availability of optimal covariate designs. In some situations, optimal estimations of both ANOVA and the regression parameters are provided. Global optimality and D-optimality criteria are mainly used in selecting the design. The standard optimality results of both discrete and continuous set-ups have been adapted, and several novel combinatorial techniques have been applied for the construction of optimum designs using Hadamard matrices, the Kronecker product, Rao-Khatri product, mixed orthogonal arrays to name a few.

This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a suitable choice of designs is of great importance; as such choices allow experimenters to extract maximum information for the unknown model parameters. The main emphasis of this monograph is to start with an assumed covariate model in combination with some standard ANOVA set-ups such as CRD, RBD, BIBD, GDD, BTIBD, BPEBD, cross-over, multi-factor, split-plot and strip-plot designs, treatment control designs, etc. and discuss the nature and availability of optimal covariate designs. In some situations, optimal estimations of both ANOVA and the regression parameters are provided. Global optimality and D-optimality criteria are mainly used in selecting the design. The standard optimality results of both discrete and continuous set-ups have been adapted, and several novel combinatorial techniques have been applied for the construction of optimum designs using Hadamard matrices, the Kronecker product, Rao-Khatri product, mixed orthogonal arrays to name a few.

This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a suitable choice of designs is of great importance; as such choices allow experimenters to extract maximum information for the unknown model parameters. The main emphasis of this monograph is to start with an assumed covariate model in combination with some standard ANOVA set-ups such as CRD, RBD, BIBD, GDD, BTIBD, BPEBD, cross-over, multi-factor, split-plot and strip-plot designs, treatment control designs, etc. and discuss the nature and availability of optimal covariate designs. In some situations, optimal estimations of both ANOVA and the regression parameters are provided. Global optimality and D-optimality criteria are mainly used in selecting the design. The standard optimality results of both discrete and continuous set-ups have been adapted, and several novel combinatorial techniques have been applied for the construction of optimum designs using Hadamard matrices, the Kronecker product, Rao-Khatri product, mixed orthogonal arrays to name a few.

This book contains international perspectives that unifies the themes of strategic management, decision theory, and data science. It contains thought-provoking presentations of case studies backed by adequate analysis adding significance to the discussions. Most of the decision-making models in use do take due advantage of collection and processing of relevant data using appropriate analytics oriented to provide inputs into effective decision-making. The book showcases applications in diverse fields including banking and insurance, portfolio management, inventory analysis, performance assessment of comparable economic agents, managing utilities in a health-care facility, reducing traffic snarls on highways, monitoring achievement of some of the sustainable development goals in a country or state, and similar other areas that showcase policy implications. It holds immense value for researchers as well as professionals responsible for organizational decisions.

This book contains international perspectives that unifies the themes of strategic management, decision theory, and data science. It contains thought-provoking presentations of case studies backed by adequate analysis adding significance to the discussions. Most of the decision-making models in use do take due advantage of collection and processing of relevant data using appropriate analytics oriented to provide inputs into effective decision-making. The book showcases applications in diverse fields including banking and insurance, portfolio management, inventory analysis, performance assessment of comparable economic agents, managing utilities in a health-care facility, reducing traffic snarls on highways, monitoring achievement of some of the sustainable development goals in a country or state, and similar other areas that showcase policy implications. It holds immense value for researchers as well as professionals responsible for organizational decisions.

This book contains international perspectives that unifies the themes of strategic management, decision theory, and data science. It contains thought-provoking presentations of case studies backed by adequate analysis adding significance to the discussions. Most of the decision-making models in use do take due advantage of collection and processing of relevant data using appropriate analytics oriented to provide inputs into effective decision-making. The book showcases applications in diverse fields including banking and insurance, portfolio management, inventory analysis, performance assessment of comparable economic agents, managing utilities in a health-care facility, reducing traffic snarls on highways, monitoring achievement of some of the sustainable development goals in a country or state, and similar other areas that showcase policy implications. It holds immense value for researchers as well as professionals responsible for organizational decisions.

The book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place. Measuring and controlling data of SDGs, data driven measurement of progress needs to be distributed to stakeholders. In this situation, the techniques used in data science, specially, in the big data analytics, play an important role rather than the traditional data gathering and manipulation techniques. This book fills this space through its twenty contributions. The contributions have been selected from those presented during the 7th International Conference on Data Science and Sustainable Development Goals organized by the Department of Statistics, University of Rajshahi, Bangladesh; and cover topics mainly on SDGs, bioinformatics, public health, medical informatics, environmental statistics, data science and machine learning. The contents of the volume would be useful to policymakers, researchers, government entities, civil society, and nonprofit organizations for monitoring and accelerating the progress of SDGs.