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The Explanatory Power of Models by Robert Franck Pdf
This book progressively works out a method of constructing models which can bridge the gap between empirical and theoretical research in the social sciences. It aims to improve the explanatory power of models. The issue is quite novel, and has benefited from a thorough examination of statistical and mathematical models, conceptual models, diagrams and maps, machines, computer simulations, and artificial neural networks.
Explanatory Model Analysis by Przemyslaw Biecek,Tomasz Burzykowski Pdf
Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
Joseph F. Hair Jr.,G. Tomas M. Hult,Christian M. Ringle,Marko Sarstedt,Nicholas P. Danks,Soumya Ray
Author : Joseph F. Hair Jr.,G. Tomas M. Hult,Christian M. Ringle,Marko Sarstedt,Nicholas P. Danks,Soumya Ray Publisher : Springer Nature Page : 208 pages File Size : 45,6 Mb Release : 2021-11-03 Category : Business & Economics ISBN : 9783030805197
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R by Joseph F. Hair Jr.,G. Tomas M. Hult,Christian M. Ringle,Marko Sarstedt,Nicholas P. Danks,Soumya Ray Pdf
Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.
The Explanatory Autonomy of the Biological Sciences by Wei Fang Pdf
This book argues for the explanatory autonomy of the biological sciences. It does so by showing that scientific explanations in the biological sciences cannot be reduced to explanations in the fundamental sciences such as physics and chemistry and by demonstrating that biological explanations are advanced by models rather than laws of nature. To maintain the explanatory autonomy of the biological sciences, the author argues against explanatory reductionism and shows that explanation in the biological sciences can be achieved without reduction. Then, he demonstrates that the biological sciences do not have laws of nature. Instead of laws, he suggests that biological models usually do the explanatory work. To understand how a biological model can explain phenomena in the world, the author proposes an inferential account of model explanation. The basic idea of this account is that, for a model to be explanatory, it must answer two kinds of questions: counterfactual-dependence questions that concern the model itself and hypothetical questions that concern the relationship between the model and its target system. The reason a biological model can answer these two kinds of questions is due to the fact that a model is a structure, and the holistic relationship between the model and its target warrants the hypothetical inference from the model to its target and thus helps to answer the second kind of question. The Explanatory Autonomy of the Biological Sciences will be of interest to researchers and advanced students working in philosophy of science, philosophy of biology and metaphysics.
Author : Pere Julia Publisher : Princeton University Press Page : 247 pages File Size : 53,5 Mb Release : 2014-07-14 Category : Language Arts & Disciplines ISBN : 9781400857944
Explanatory Models in Linguistics by Pere Julia Pdf
Pere Julia questions the recourse of contemporary linguists, psycholinguists, and philosophers to an idealized speaker-listener and maintains that there is no way to be sure of the organizing principles for linguistic data other than going to the sources of these data, i.e., speakers, listeners, and the circumstances under which they interact in actual situations. Originally published in 1983. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
Richard A. DeFusco,Dennis W. McLeavey,Jerald E. Pinto,David E. Runkle,Mark J. P. Anson
Author : Richard A. DeFusco,Dennis W. McLeavey,Jerald E. Pinto,David E. Runkle,Mark J. P. Anson Publisher : John Wiley & Sons Page : 635 pages File Size : 41,7 Mb Release : 2015-10-15 Category : Business & Economics ISBN : 9781119104605
Quantitative Investment Analysis by Richard A. DeFusco,Dennis W. McLeavey,Jerald E. Pinto,David E. Runkle,Mark J. P. Anson Pdf
Your complete guide to quantitative analysis in the investment industry Quantitative Investment Analysis, Third Edition is a newly revised and updated text that presents you with a blend of theory and practice materials to guide you through the use of statistics within the context of finance and investment. With equal focus on theoretical concepts and their practical applications, this approachable resource offers features, such as learning outcome statements, that are targeted at helping you understand, retain, and apply the information you have learned. Throughout the text's chapters, you explore a wide range of topics, such as the time value of money, discounted cash flow applications, common probability distributions, sampling and estimation, hypothesis testing, and correlation and regression. Applying quantitative analysis to the investment process is an important task for investment pros and students. A reference that provides even subject matter treatment, consistent mathematical notation, and continuity in topic coverage will make the learning process easier—and will bolster your success. Explore the materials you need to apply quantitative analysis to finance and investment data—even if you have no previous knowledge of this subject area Access updated content that offers insight into the latest topics relevant to the field Consider a wide range of subject areas within the text, including chapters on multiple regression, issues in regression analysis, time-series analysis, and portfolio concepts Leverage supplemental materials, including the companion Workbook and Instructor's Manual, sold separately Quantitative Investment Analysis, Third Edition is a fundamental resource that covers the wide range of quantitative methods you need to know in order to apply quantitative analysis to the investment process.
Uncertainty in the predictions of science when applied to the environment is an issue of great current relevance in relation to the impacts of climate change, protecting against natural and man-made disasters, pollutant transport and sustainable resource management. However, it is often ignored both by scientists and decision makers, or interpreted as a conflict or disagreement between scientists. This is not necessarily the case, the scientists might well agree, but their predictions would still be uncertain and knowledge of that uncertainty might be important in decision making. Environmental Modelling: An Uncertain Future? introduces students, scientists and decision makers to: the different concepts and techniques of uncertainty estimation in environmental prediction the philosophical background to different concepts of uncertainty the constraint of uncertainties by the collection of observations and data assimilation in real-time forecasting techniques for decision making under uncertainty. This book will be relevant to environmental modellers, practitioners and decision makers in hydrology, hydraulics, ecology, meteorology and oceanography, geomorphology, geochemistry, soil science, pollutant transport and climate change. A companion website for the book can be found at www.uncertain-future.org.uk
Linear Models in Statistics by Alvin C. Rencher,G. Bruce Schaalje Pdf
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
The Power of Passive Investing by Richard A. Ferri Pdf
A practical guide to passive investing Time and again, individual investors discover, all too late, that actively picking stocks is a loser's game. The alternative lies with index funds. This passive form of investing allows you to participate in the markets relatively cheaply while prospering all the more because the money saved on investment expenses stays in your pocket. In his latest book, investment expert Richard Ferri shows you how easy and accessible index investing is. Along the way, he highlights how successful you can be by using this passive approach to allocate funds to stocks, bonds, and other prudent asset classes. Addresses the advantages of index funds over portfolios that are actively managed Offers insights on index-based funds that provide exposure to designated broad markets and don't make bets on individual securities Ferri is also author of the Wiley title: The ETF Book and co-author of The Bogleheads' Guide to Retirement Planning If you're looking for a productive investment approach that won't take all of your time to implement, then The Power of Passive Investing is the book you need to read.
Econometric Analysis of Cross Section and Panel Data, second edition by Jeffrey M. Wooldridge Pdf
The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
EU membership involves political and economic reforms which influence financial markets in the new member states. This study empirically explores and quantifies the effects of EU accession on the risk and return of equity markets in eight Central and Eastern European markets joining the EU in 2004. The study also incorporates a review of how the influence of macroeconomic variables and the level of integration with global and European markets change as a result of EU membership. Based on empirical tests using weekly data over ten years, this study concludes that EU membership results in a significant decline in equity market volatility and a significant increase in risk-adjusted, but not absolute, equity returns. Furthermore, the study suggests that equity markets in new EU member states become increasingly influenced by global rather than local macroeconomic factors after the EU accession and that the level of integration with global markets increases.
Strategic Deals in Emerging Capital Markets by Irina Ivashkovskaya,Svetlana Grigorieva,Eugene Nivorozhkin Pdf
The industrial development of emerging markets has been a powerful driver for mergers and acquisitions. The contributions collected in this book assess major M&A deals in the largest emerging capital markets (Brazil, Russia, India, China) and their role in shareholder value creation in the markets’ specific business environments. In addition, the book explores various dimensions of M&A deals in order to summarize the main trends in corporate control markets in the largest emerging countries, and how they differ from those in developed countries; to identify deal-performance relationships and the determinants of success or failure; to reveal the drivers for the premium in M&A deals; and to capture market responses to different M&A strategies. By doing so, the book makes a significant contribution to the literature, which has to date largely focused on developed markets.
Mixed Effects Models for Complex Data by Lang Wu Pdf
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
