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| Author | : Georges Lucioni Edison Nguefack Tsague |
| Publisher | : |
| Total Pages | : 156 |
| Release | : 2006 |
| Genre | : |
| ISBN | : 9783865378446 |
| Author | : Islam Azzam |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
Model selection uncertainty adds to the variability in the coefficient estimates when small samples are used because model-selection criteria perform poorly in small samples. Previous literatures account for model-selection uncertainty to improve inference by endogenizing the lag order selection using bootstrap methods. This paper shows that all bootstrap methods fail in cases that are most common in macroeconomic applications. As the maximum eigenvalue of the vector autoregressive model gets closer to one, the bias of the impulse response estimates increases. As a result, the standard bootstrap resampling produces low interval coverage accuracy while bootstrap subsampling produces zero coverage. A proposed solution for this problem is using the first-order bias correction with bootstrap interval for impulse response estimates, which corrects for the first and second order bias of these estimators. This dramatically improves the interval coverage accuracy for impulse response estimates.
| Author | : Islam Azzam |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
Conventional asymptotic and bootstrap methods for finite-order autoregressive models condition on the estimated lag-order of the model, which is later, used to construct the error bands for impulse response functions. Even if the estimated lag order is believed to be correct, this procedure ignores the sampling uncertainty of the lag order. An earlier study by Kilian (1998) introduced an endogenous lag order bootstrap algorithm that reflected the true extent of sampling uncertainty in the regression estimates. Applications of Kilian's method to vector autoregressive (VAR) and vector error correction (VEC) assumed that the true cointegration rank is known. This paper modifies the application of kilian's method on VEC models by endogenizing the cointegration rank besides the lag order. Monte Carlo simulations results from two U.S. economy models show that ignoring cointegration rank uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for VEC impulse response estimates. Endogenizing the cointegration rank choice is shown to improve coverage accuracy at low additional computational cost.
| Author | : Louis Anthony Cox Jr. |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 564 |
| Release | : 2012-12-06 |
| Genre | : Business & Economics |
| ISBN | : 1461508479 |
Risk Analysis: Foundations, Models, and Methods fully addresses the questions of "What is health risk analysis?" and "How can its potentialities be developed to be most valuable to public health decision-makers and other health risk managers?" Risk analysis provides methods and principles for answering these questions. It is divided into methods for assessing, communicating, and managing health risks. Risk assessment quantitatively estimates the health risks to individuals and to groups from hazardous exposures and from the decisions or activities that create them. It applies specialized models and methods to quantify likely exposures and their resulting health risks. Its goal is to produce information to improve decisions. It does this by relating alternative decisions to their probable consequences and by identifying those decisions that make preferred outcomes more likely. Health risk assessment draws on explicit engineering, biomathematical, and statistical consequence models to describe or simulate the causal relations between actions and their probable effects on health. Risk communication characterizes and presents information about health risks and uncertainties to decision-makers and stakeholders. Risk management applies principles for choosing among alternative decision alternatives or actions that affect exposure, health risks, or their consequences.
| Author | : David Fletcher |
| Publisher | : Springer |
| Total Pages | : 107 |
| Release | : 2019-01-17 |
| Genre | : Mathematics |
| ISBN | : 3662585413 |
This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
| Author | : |
| Publisher | : Springer |
| Total Pages | : 7493 |
| Release | : 2016-05-18 |
| Genre | : Law |
| ISBN | : 1349588024 |
The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product.
| Author | : Juegang Hu |
| Publisher | : |
| Total Pages | : |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
Recent advances in $ell_1$-regularization methods have proved to be very useful for high dimensional model selection and inference. In the high dimensional regression context, the lasso and its extensions have been successfully employed to identify parsimonious sets of predictors It is well known that the lasso has the advantage of performing model selection and estimation simultaneously. It is less well understood how much uncertainty the lasso estimates may have due to small sample sizes. To model this uncertainty, we present a method, called the "contour Bayesian lasso" for the purposes of constructing joint credible regions for regression parameters. The contour Bayesian lasso is an extension of a recent approach called the "Bayesian lasso" which in turn is based on the Bayesian interpretation of the lasso. The Bayesian lasso uses a Gibbs sampler to generate from the Bayesian lasso posterior and is thus a convenient approach for quantifying uncertainty of lasso estimates. We give theoretical results regarding the optimality of the contour approach, study posterior consistency and the convergence of the Gibbs sampler. We also analyze the frequentist properties of the Bayesian lasso approach. A theoretical analysis of how the convergence of the Gibbs sampler depends on the dimensionality and sample size is undertaken. Our methodology is also illustrated on simulated and real data. We demonstrate that our posterior credible method has good coverage, and thus yields more accurate sparse solutions when the sample size is small. Real life examples are given for the South African prostate cancer data and the diabetes data set.
| Author | : David Ruppert |
| Publisher | : Cambridge University Press |
| Total Pages | : 408 |
| Release | : 2003-07-14 |
| Genre | : Mathematics |
| ISBN | : 9780521785167 |
Even experts on semiparametric regression should find something new here.
| Author | : Theo K. Dijkstra |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 149 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 3642615643 |
In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.
| Author | : Christopher H. Schmid |
| Publisher | : CRC Press |
| Total Pages | : 476 |
| Release | : 2020-09-08 |
| Genre | : Mathematics |
| ISBN | : 1351645714 |
Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas, including medicine and health, social sciences, education, psychology, ecology, and economics. Recent years have seen an explosion of methods for handling complexities in meta-analysis, including explained and unexplained heterogeneity between studies, publication bias, and sparse data. At the same time, meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups, to complex observational studies, to assessing heterogeneity of effects, and to survival and multivariate outcomes. Many of these methods are statistically complex and are tailored to specific types of data. Key features Rigorous coverage of the full range of current statistical methodology used in meta-analysis Comprehensive, coherent, and unified overview of the statistical foundations behind meta-analysis Detailed description of the primary methods for both univariate and multivariate data Computer code to reproduce examples in chapters Thorough review of the literature with thousands of references Applications to specific types of biomedical and social science data This book is for a broad audience of graduate students, researchers, and practitioners interested in the theory and application of statistical methods for meta-analysis. It is written at the level of graduate courses in statistics, but will be of interest to and readable for quantitative scientists from a range of disciplines. The book can be used as a graduate level textbook, as a general reference for methods, or as an introduction to specialized topics using state-of-the art methods.
