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"Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The treatment is both rigorous and broadly accessible, distinguishing this text from much of the current literature and providing students, researchers, and practitioners with a strong foundation for the often-daunting task of solving real-world problems." "The book includes over 130 examples, Web links to software and data sets, more than 250 exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization."--Jacket

Getting started -- PCA with more than two variables -- Scaling of data -- Inferential procedures -- Putting it all together -- hearing loss I -- Operations with group data -- Vector interpretation I: simplifications and inferential techniques -- Vector interpretation II: rotation -- A case history-hearing loss II -- Singular value decomposition: multidimensional scaling I -- Distance models: multidimensional scaling II -- Linear models I: regression; PCA of predictor Variables -- Linear models II: analysis of variance; PCA of response variables -- Other applications of PCA -- Flatland: special procedures for two dimensions -- Odds and ends -- What is factor analysis anyhow? -- Other competitors.

chapter 1 Modeling of Equity-linked Insurance -- chapter 2 Elementary Stochastic Calculus -- chapter 3 Monte Carlo Simulations of Investment Guarantees -- chapter 4 Pricing and Valuation -- chapter 5 Risk Management - Reserving and Capital Requirement -- chapter 6 Risk Management - Dynamic Hedging -- chapter 7 Advanced ComputationalMethods.

"This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. However, the models in this book can be used to analyse any kind of data. The data are analysed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers' learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research"--

"Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics & modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science. It is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels"--

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods

"Provides a unified approach to social research, integrating both agent-based models and behavioral studies. Introduces the reader to all the concepts, tools and references that are required for conducting research in behavioral computational social science"--

Frontmatter -- Overview of Multivariate and Regression Methods -- The Seven Habits of Highly Effective Quants: A Review of Elementary Statistics Using Matrix Algebra -- Fundamentals of Matrix Algebra -- Factor Analysis and Related Methods: Quintessentially Multivariate -- Multivariate Graphics -- Canonical Correlation: The Underused Method -- Hotelling's as the Simplest Case of Multivariate Inference -- Multivariate Analysis of Variance -- Multiple Regression and the General Linear Model -- Appendices: Statistical Tables -- Name Index -- Subject Index.

An effective technique for data analysis in the social sciencesThe recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings a

Introduction : distributions and inference for categorical data -- Describing contingency tables -- Inference for contingency tables -- Introduction to generalized linear models -- Logistic regression -- Building and applying logistic regression models -- Logit models for multinomial responses -- Loglinear models for contingency tables -- Building and extending loglinear/logit models -- Models for matched pairs -- Analyzing repeated categorical response data -- Random effects : generalized linear mixed models for categorical responses -- Other mixture models for categorical data -- Asymptotic theory for parametric models -- Alternative estimation theory for parametric models -- Historical tour of categorical data analysis.

Applied Regression Including Computing and Graphics; Contents; Preface; PART I INTRODUCTION; 1 Looking Forward and Back; 2 Introduction to Regression; 3 Introduction to Smoothing; 4 Bivariate Distributions; 5 Two-Dimensional Plots; PART II. TOOLS; 6 Simple Linear Regression; 7 Introduction to Multiple Linear Regression; 8 Three-Dimensional Plots; 9 Weights and Lack-of-Fit; 10 Understanding Coefficients; 11 Relating Mean Functions; 12 Factors and Interactions; 13 Response Transformations; 14 Diagnostics I: Curvature and Nonconstant Variance; 15 Diagnostics II: Influence and Outliers.