Advances in Statistical Analysis and Statistical Computing

Econometric Forecasting and High-frequency Data Analysis

Roberto S. Mariano, Yiu Kuen Tse

This important book consists of surveys of high-frequency financial data analysis and econometric forecasting, written by pioneers in these areas including Nobel laureate Lawrence Klein. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and High-Frequency Data Analysis Workshop at the Institute for Mathematical Science, National University of Singapore in May 2006. They will be of interest to researchers working in macroeconometrics as well as financial econometrics. Moreover, readers will find these chapters useful as a guide to the literature as well as suggestions for future research. Sample Chapter(s). Foreword (32 KB). Chapter 1: Forecast Uncertainty, Its Representation and Evaluation* (97 KB). Contents: Forecasting Uncertainty, Its Representation and Evaluation (K F Wallis); The University of Pennsylvania Models for High-Frequency Macroeconomic Modeling (L R Klein & S Ozmucur); Forecasting Seasonal Time Series (P H Franses); Car and Affine Processes (C Gourieroux); Multivariate Time Series Analysis and Forecasting (M Deistler). Readership: Professionals and researchers in econometric forecasting and financial data analysis.

Large Sample Asymptotic Expansions for General Linear Simultaneous Systems Under Misspecification

Roberto S. Mariano

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On the Effects of Multicollinearity Upon the Properties of Structural Coefficient Estimators

Roberto S. Mariano

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On Distributions and Moments of Single-equation Estimators in a Set of Simultaneous Linear Stochastic Equations

Roberto S. Mariano

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A Macro-economic Model of the Philippines, 1950-1969

José Encarnación, Roberto S. Mariano, Romeo M. Bautista

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Testing Under Non-standard Conditions in Frequency Domain

With Applications to Markov Regime Switching Models of Exchange Rates and the Federal Funds Rate

Fangxiong Gong, Roberto S. Mariano

"We propose two test statistics in the frequency domain and derive their exact asymptotic null distributions under the condition of unidentified nuisance parameters. The proposed methods are particularly applicable in unobserved components models. Also, it is shown that the tests have considerable power when applied to a class of Markov regime switching models. We show that, after transforming the Markov regime switching model into the frequency domain representation we only have to face the issue of unidentified nuisance parameters in a nonlinear context. The singularity problem disappears. Compared to Hansen's (1992, 1996) LR-bound test of the same Markov regime switching model, our LM test performs better in terms of finite sample power, except in the special case of the Markov switching model in which the model becomes a Normal mixture model. Our test needs only a one-dimensional grid search while Hansen's (1992, 1996) test requires a three-dimensional grid search. The LM test is applied to Markov regime switching models of exchange rates and the Federal Funds rate. We used the same exchange rates data in Engel and Hamilton (1990). The null of random walk is not rejected in the exchange rates model. The null is rejected for the Federal Funds rate in subsample periods 1955:1-1979:9 and 1982:10-1995:11"--Abstract

Generalized Additive Models

An Introduction with R

Simon Wood

Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.

Generalized Linear Models, Second Edition

P. McCullagh, John A. Nelder

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.

A Course of Pure Mathematics

G. H. Hardy

Hardy's Pure Mathematics has been a classic textbook since its publication in 1908. This reissue will bring it to the attention of a whole new generation of mathematicians.

Principles of Multivariate Analysis

W. J. Krzanowski

Multivariate analysis is necessary whenever more than one characteristic is observed on each individual under study. Applications arise in very many areas of study. This book provides a comprehensive introduction to available techniques for analysing date of this form, written in a style that should appeal to non-specialists as well as to statisticians. In particular, geometric intuition is emphasized in preference to algebraic manipulation wherever possible. The new edition includes a survey of the most recent developments in the subject.

Applied Multiple Regression/correlation Analysis for the Behavioral Sciences

Jacob Cohen, Patricia Cohen

This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

Statistics for Long-Memory Processes

Jan Beran

Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context. Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.

Fundamentals of Statistics for Researchers and Data Analysts

Shashi A. Chiplonkar, Jayashree S. Date

Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors. - Presents an application-oriented approach towards statistical data analysis with minimal theory - Highlights the importance of prerequisites of data for applying particular statistical methods - Contains a variety of case studies and solved examples using software such as Excel and SPSS, along with the actual datasets

New Advances in Statistics and Data Science

Ding-Geng Chen, Zhezhen Jin, Gang Li, Yi Li, Aiyi Liu, Yichuan Zhao

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EPA 440/1

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Dosage Form Design Parameters

Volume II

Unknown author

Dosage Form Design Parameters, Volume II, examines the history and current state of the field within the pharmaceutical sciences, presenting key developments. Content includes drug development issues, the scale up of formulations, regulatory issues, intellectual property, solid state properties and polymorphism. Written by experts in the field, this volume in the Advances in Pharmaceutical Product Development and Research series deepens our understanding of dosage form design parameters. Chapters delve into a particular aspect of this fundamental field, covering principles, methodologies and the technologies employed by pharmaceutical scientists. In addition, the book contains a comprehensive examination suitable for researchers and advanced students working in pharmaceuticals, cosmetics, biotechnology and related industries. - Examines the history and recent developments in drug dosage forms for pharmaceutical sciences - Focuses on physicochemical aspects, prefomulation solid state properties and polymorphism - Contains extensive references for further discovery and learning that are appropriate for advanced undergraduates, graduate students and those interested in drug dosage design

Environmental Modelling with Contemporary Statistics

Learning, Directionality, and Space-Time Dynamics

Andriëtte Bekker, Priyanka Nagar, Johan Ferreira, Barend Erasmus, Abel Ramoelo

This book places an emphasis upon statistical methodology, data interpretation, and futureproofing with the intention of advancing statistics for the environment, ecology, and environmental health, in addition to environmental theory and practice, via the application of reliable statistics. With a focus on advances in statistical methodology and application within the environmental sciences, the overarching purpose of this volume is to illuminate current trends, stimulate a focus on, and connect multidisciplinary domains originating from and within statistical analysis, development, and research. Given that the contributions consist of current improvements and new innovations in climate and environmental science research that are based on statistical theory, researchers can derive inspiration for future advancements or similar analyses on other environmental data. Authored by internationally renowned scholars, this book is organized into three parts: Part I on Supervised and Unsupervised Learning, Part II on Directional Statistics, and Part III on Spatial and Temporal Modeling. Primarily intended as a reference book for academic researchers and graduate-level students in statistics as well as multidisciplinary domains, the chapters reflect a shared commitment to advancing methodological rigor while addressing real-world environmental concerns. They illustrate how environmental complexity drives the evolution of statistical thinking—and how statistical insight, in turn, informs meaningful action. Key Features Emphasizes the ongoing necessity to progress basic statistical theory and explores its relevance to environmental research Employs multidisciplinary approaches and applications, appealing to a broader audience beyond solely theoretical statistics Enhances the collective understanding of the ecosystem’s diverse perspectives to ensure the welfare of present and future generations Written by renowned subject matter experts and researchers, making it appealing to scholars from diverse fields The statistical framework is not limited to a single methodology based on data complexity but promotes different techniques

Popular Science Monthly and World's Advance

Unknown author

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