Biased Sampling Problems with Applications to Cross-section Data
Author | : Micha Mandel |
Publisher | : |
Total Pages | : 228 |
Release | : 2004 |
Genre | : Multivariate analysis |
ISBN | : |
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Author | : Micha Mandel |
Publisher | : |
Total Pages | : 228 |
Release | : 2004 |
Genre | : Multivariate analysis |
ISBN | : |
Author | : Jing Qin |
Publisher | : |
Total Pages | : 624 |
Release | : 2017 |
Genre | : Economics |
ISBN | : 9789811048555 |
Author | : Jing Qin |
Publisher | : Springer |
Total Pages | : 626 |
Release | : 2017-06-14 |
Genre | : Business & Economics |
ISBN | : 9811048568 |
This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others.
Author | : Deep Mukherjee |
Publisher | : Springer Nature |
Total Pages | : 625 |
Release | : 2024-01-03 |
Genre | : Business & Economics |
ISBN | : 9819949025 |
This book is a collection of 20 chapters on chosen topics from cross-section and panel data econometrics. It explores both theoretical and practical aspects of selected cutting-edge techniques which are gaining popularity among applied econometricians, while following the motto of “keeping things simple”. Each chapter gives a basic introduction to one such method, directs readers to supplementary references, and shows an application. The book takes into account that—A: The field of econometrics is evolving very fast and leading textbooks are trying to cover some of the recent developments in revised editions. This book offers basic introduction to state-of-the-art techniques and recent advances in econometric models with detailed applications from various developing and developed countries. B: An applied researcher or practitioner may prefer reference books with a simple introduction to an advanced econometric method or model with no theorems but with a longer discussion on empirical application. Thus, an applied econometrics textbook covering these cutting-edge methods is highly warranted; a void this book attempts to fills.The book does not aim at providing a comprehensive coverage of econometric methods. The 20 chapters in this book represent only a sample of the important topics in modern econometrics, with special focus on econometrics of cross-section and panel data, while also recognizing that it is not possible to accommodate all types of models and methods even in these two categories. The book is unique as authors have also provided the theoretical background (if any) and brief literature review behind the empirical applications. It is a must-have resource for students and practitioners of modern econometrics.
Author | : National Research Council |
Publisher | : National Academies Press |
Total Pages | : 191 |
Release | : 2013-09-03 |
Genre | : Mathematics |
ISBN | : 0309287812 |
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Author | : Edward W. Frees |
Publisher | : Cambridge University Press |
Total Pages | : 492 |
Release | : 2004-08-16 |
Genre | : Business & Economics |
ISBN | : 9780521535380 |
An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.
Author | : Kim P. Huynh |
Publisher | : Emerald Group Publishing |
Total Pages | : 344 |
Release | : 2019-04-10 |
Genre | : Business & Economics |
ISBN | : 1787567257 |
This volume of Advances in Econometrics contains a selection of papers presented at the 'Econometrics of Complex Survey Data: Theory and Applications' conference organized by the Bank of Canada, Ottawa, Canada, from October 19-20, 2017.
Author | : Jorge M. Uribe |
Publisher | : Springer Nature |
Total Pages | : 63 |
Release | : 2020-03-30 |
Genre | : Business & Economics |
ISBN | : 3030445046 |
This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R.
Author | : Muhammad Abdullah |
Publisher | : |
Total Pages | : 10 |
Release | : 2017 |
Genre | : |
ISBN | : |
The inclusion of lagged dependent variable in the list of explanatory variables introduces the specific estimation problems even the generalized least squares estimator for the dynamic panel data models allowing cross sectional heteroscedasticity becomes biased and inconsistent. In this study, the analytical expressions for the inconsistency have been derived in the first order autoregressive case. A comparison between asymptotic bias and small sample simulated bias has also been carried out. The analytical biases emerged coincident with or a little above the small sample simulated biases. The closeness of the two types of biases mainly depends on coefficient of lagged dependent variable (y) and the number of cross sectional units N.