Aman Ullah - Böcker

Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty.In Advances in Info-Metrics, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah bring together a group of thirty experts to expand the study of info-metrics across the sciences and demonstrate how to solve problems using this interdisciplinary framework. Building on the theoretical underpinnings of info-metrics, the volume sheds new light on statistical inference, information, and general problem solving. The book explores the basis of information-theoretic inference and its mathematical and philosophical foundations. It emphasizes the interrelationship between information and inference and includes explanations of model building, theory creation, estimation, prediction, and decision making. Each of the nineteen chapters provides the necessary tools for using the info-metrics framework to solve a problem. The collection covers recent developments in the field, as well as many new cross-disciplinary case studies and examples. Designed to be accessible for researchers, graduate students, and practitioners across disciplines, this book provides a clear, hands-on experience for readers interested in solving problems when presented with incomplete and imperfect information.

This book provides a comprehensive and unified treatment of finite sample statistics and econometrics, a field that has evolved in the last five decades. Within this framework, this is the first book which discusses the basic analytical tools of finite sample econometrics, and explores their applications to models covered in a first year graduate course in econometrics, including repression functions, dynamic models, forecasting, simultaneous equations models, panel data models, and censored models. Both linear and nonlinear models, as well as models with normal and non-normal errors, are studied.Finite sample results are extremely useful for applied researchers doing proper econometric analysis with small or moderately large sample data. Finite sample econometrics also provides the results for very large (asymptotic) samples. This book provides simple and intuitive presentations of difficult concepts, unified and heuristic developments of methods, and applications to various econometric models. It provides a new perspective on teaching and research in econometrics, statistics, and other applied subjects.

This text provides a comprehensive and unified treatment of finite sample statistics and econometrics, a field that has evolved since the 1950s. Within this framework, the book discusses the basic analytical tools of finite sample econometrics and explores their applications to models covered in a first year graduate course in econometrics, including repression functions, dynamic models, forecasting, simultaneous equations models, panel data models and censored models. Both linear and nonlinear models, as well as models with normal and non-normal errors, are studied. Finite sample results are extremely useful for applied researchers doing proper econometric analysis with small or moderately large sample data. Finite sample econometrics also provides the results for very large (asymptotic) samples. This book provides simple and intuitive presentations of difficult concepts, unified and heuristic developments of methods and applications to various econometric models. It provides a new perspective on teaching and research in econometrics, statistics and other applied subjects.

This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. These data-driven models seek to replace the "classical " parametric models of the past, which were rigid and often linear. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures. They provide a balanced view of new developments in the analysis and modeling of applied sciences with cross-section, time series, panel, and spatial data sets. The major topics of the volume include: the methodology of semiparametric models and special regressor methods; inverse, ill-posed, and well-posed problems; different methodologies related to additive models; sieve regression estimators, nonparametric and semiparametric regression models, and the true error of competing approximate models; support vector machines and their modeling of default probability; series estimation of stochastic processes and some of their applications in Econometrics; identification, estimation, and specification problems in a class of semilinear time series models; nonparametric and semiparametric techniques applied to nonstationary or near nonstationary variables; the estimation of a set of regression equations; and a new approach to the analysis of nonparametric models with exogenous treatment assignment.

Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, engineering, sociology, psychology, and information technology. It supplies a geometric proof of an extended Gauss-Markov theorem, approaches for the design and implementation of sample surveys, advances in the theory of Neyman's smooth test, and methods for pre-test and biased estimation. It includes discussions ofsample size requirements for estimation in SUR models, innovative developments in nonparametric models, and more.

This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.

Originally published in 1981, this book considers one particular area of econometrics- the linear model- where significant recent advances have been made. It considers both single and multiequation models with varying co-efficients, explains the various theories and techniques connected with these and goes on to describe the various applications of the models. Whilst the detailed explanation of the models will interest primarily econometrics specialists, the implications of the advances outlined and the applications of the models will intrest a wide range of economists.

Originally published in 1981, this book considers one particular area of econometrics- the linear model- where significant recent advances have been made. It considers both single and multiequation models with varying co-efficients, explains the various theories and techniques connected with these and goes on to describe the various applications of the models. Whilst the detailed explanation of the models will interest primarily econometrics specialists, the implications of the advances outlined and the applications of the models will intrest a wide range of economists.

This book systematically and thoroughly covers the vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades. Within this framework this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g. regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Nonparametric and semiparametric methods potentially offer considerable reward to applied researchers, owing to the methods' ability to adapt to many unknown features of the data. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of the modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.

This book systematically and thoroughly covers the vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades. Within this framework this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g. regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Nonparametric and semiparametric methods potentially offer considerable reward to applied researchers, owing to the methods' ability to adapt to many unknown features of the data. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of the modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.

This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.

Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, engineering, sociology, psychology, and information technology. It supplies a geometric proof of an extended Gauss-Markov theorem, approaches for the design and implementation of sample surveys, advances in the theory of Neyman's smooth test, and methods for pre-test and biased estimation. It includes discussions ofsample size requirements for estimation in SUR models, innovative developments in nonparametric models, and more.

With a pressing need for more sustainable packaging solutions, biopolymers are a promising alternative to traditional packaging materials as they are renewable, biodegradable, and often compostable. Green Biopolymers for Packaging Applications provides a comprehensive examination of the present state and prospective advancements in green biopolymers for packaging. Covering the development, characterization, properties, and performance of biopolymers customized for packaging purposes, the book's contributors explores their environmental impacts and economic benefits.Key features of the book include:Offers a detailed examination of the present state and forthcoming prospects of green biopolymers in packaging.Explores a multitude of applications where biopolymers can be utilized in packaging solutions.Evaluates the ecological footprint, economic advantages, and hurdles associated with the adoption of green biopolymers for packaging purposes.Delves into forthcoming outlooks and promising avenues for research within the realm of biopolymer-based packaging.Intended as an essential resource for professionals in academia, industry, and government, its goal is to offer a complete understanding of the opportunities and challenges related to employing biopolymers in packaging solutions.

This book provides a comprehensive overview of the use of nanotechnology in food preservation, food packaging, and the development of novel preservation methods. The chapters focus on applications of nanotechnology in active food packaging, modified atmosphere packaging, and the development of nanocomposite films and coatings for food packaging. Additionally, the book further explores the potential of nanosensors and biosensors for real-time monitoring of food quality and safety and provides insightful case studies showcasing successful nanotechnology applications in the food industry. It also discusses the potential challenges and limitations of using nanotechnology in food preservation, as well as the regulatory and safety concerns that need to be addressed. Furthermore, the book examines consumer perceptions and acceptance of nanotechnology in food preservation and concludes by comparing nanotechnology-based methods with traditional approaches, paving the way for future directions and advancements in this field. This book is a valuable resource for researchers, practitioners, and policymakers in the food industry.Key Features:Provides an overview of application of nanotechnology in food packaging and preservationDiscusses the potential of nanotechnology in active food packaging, modified atmosphere packaging, and nanocomposite films for packagingReviews the potential of nanosensors and biosensors for real-time monitoring of food quality and safetyExplores potential challenges and limitations related to nanotechnology in food preservationExamines consumer perceptions and acceptance of nanotechnology in food preservationPresents real-world case studies on the use of successful nanotechnology implementations in the food industry

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.

Originally published in 1981, this book considers one particular area of econometrics- the linear model- where significant recent advances have been made. It considers both single and multiequation models with varying co-efficients, explains the various theories and techniques connected with these and goes on to describe the various applications of the models. Whilst the detailed explanation of the models will interest primarily econometrics specialists, the implications of the advances outlined and the applications of the models will intrest a wide range of economists.

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.

In recent years, the use of renewable raw materials and natural biopolymers has increased significantly to overcome the issues associated with environmental pollution and dwindling fossil fuel resources. Lipids and proteins are inexpensive, renewable raw materials, which are considered ideal feedstock for the development of a variety of functional materials. This book provides an international viewpoint on recent developments in the utilisation of lipids and proteins together, for the development of functional materials in food engineering, environmental and industry applications. This book presents the cutting-edge research in the utilization of lipids and proteins in food, cosmetics, therapeutics, food packaging, water remediation, biofuels, lubricants, biomaterials, and composite preparations. Researchers, scientists, engineers and students working on lipids and proteins derived materials will benefit from this book, which is highly application oriented. Focusing on the latest developments in the field, this will be the first book to describe lipids and proteins together, allowing it to act as a single reference for researchers working in this area.

Over the last three decades much research in empirical and theoretical economics has been carried on under various assumptions. For example a parametric functional form of the regression model, the heteroskedasticity, and the autocorrelation is always as­ sumed, usually linear. Also, the errors are assumed to follow certain parametric distri­ butions, often normal. A disadvantage of parametric econometrics based on these assumptions is that it may not be robust to the slight data inconsistency with the particular parametric specification. Indeed any misspecification in the functional form may lead to erroneous conclusions. In view of these problems, recently there has been significant interest in 'the semiparametric/nonparametric approaches to econometrics. The semiparametric approach considers econometric models where one component has a parametric and the other, which is unknown, a nonparametric specification (Manski 1984 and Horowitz and Neumann 1987, among others). The purely non­ parametric approach, on the other hand, does not specify any component of the model a priori. The main ingredient of this approach is the data based estimation of the unknown joint density due to Rosenblatt (1956). Since then, especially in the last decade, a vast amount of literature has appeared on nonparametric estimation in statistics journals. However, this literature is mostly highly technical and this may partly be the reason why very little is known about it in econometrics, although see Bierens (1987) and Ullah (1988).