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Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Throughout the physical and social sciences researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years the number and variety of distributions to choose from-all with their own formulas tables diagrams and general properties-continue to create problems. For a specific application which of the dozens of distributions should one use? What if none of them fit well?Fitting Statistical Distributions helps answer those questions. Focusing on techniques used successfully across many fields the authors present all of the relevant results related to the Generalized Lambda Distribution (GLD) the Generalized Bootstrap (GB) and Monte Carlo simulation (MC). They provide the tables algorithms and computer programs needed for fitting continuous probability distributions to data in a wide variety of circumstances-covering bivariate as well as univariate distributions and including situations where moments do not exist. Regardless of your specific field-physical science social science or statistics practitioner or theorist-Fitting Statistical Distributions is required reading. It includes wide-ranging applications illustrating the methods in practice and offers proofs of key results for those involved in theoretical development. Without it you may be using obsolete methods wasting time and risking incorrect results. | Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

GBP 59.99
1

Decision Support System and Automated Negotiations

Decision Support System and Automated Negotiations

Decision support systems are developed for integrated pest and disease management and nutrition management using open-source technologies as java android and low-cost hardware devices like Arduino micro controller. This text discusses the techniques to convert agricultural knowledge in the context of ontology and assist grape growers by providing this knowledge through decision support system. The key features of the book are: Presents the design & development of an ontology-based decision support system for integrated crop management. Discusses the techniques to convert agricultural knowledge in text to ontology. Focuses on an extensive study of various e-Negotiation protocols for automated negotiations Provides an architecture for predicting the opponent’s behaviour and various factors which affect the process of negotiation. The text is primarily written for graduate students professionals and academic researchers working in the fields of computer science and engineering agricultural science and information technology. | Decision Support System and Automated Negotiations

GBP 89.99
1

Handbook of Empirical Economics and Finance

Handbook of Empirical Economics and Finance

Handbook of Empirical Economics and Finance explores the latest developments in the analysis and modeling of economic and financial data. Well-recognized econometric experts discuss the rapidly growing research in economics and finance and offer insight on the future direction of these fields. Focusing on micro models the first group of chapters describes the statistical issues involved in the analysis of econometric models with cross-sectional data often arising in microeconomics. The book then illustrates time series models that are extensively used in empirical macroeconomics and finance. The last set of chapters explores the types of panel data and spatial models that are becoming increasingly significant in analyzing complex economic behavior and policy evaluations. This handbook brings together both background material and new methodological and applied results that are extremely important to the current and future frontiers in empirical economics and finance. It emphasizes inferential issues that transpire in the analysis of cross-sectional time series and panel data-based empirical models in economics finance and related disciplines.

GBP 69.99
1

Multilevel Modeling Using R

Multilevel Modeling Using R

Like its bestselling predecessor Multilevel Modeling Using R Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models including rank based heavy tailed distributions and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.

GBP 56.99
1