Statistical Learning From A Regression Perspective

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Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.

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Genre : Mathematics
Author : Richard A. Berk
Publisher : Springer Science & Business Media
Release : 2008-06-14
File : 373 Pages
ISBN-13 : 9780387775012


Statistical Learning From A Regression Perspective

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Genre : Regression analysis
Author : Richard A. Berk
Publisher :
Release : 2018
File : 358 Pages
ISBN-13 : 7519244636


Statistical Learning From A Regression Perspective Third Edition

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Genre : Regression analysis
Author : Richard A. Berk
Publisher :
Release : 2024
File : 0 Pages
ISBN-13 : 7523211321


Industrial And Labor Relations Review

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Genre : Industrial relations
Author :
Publisher :
Release : 2014
File : 576 Pages
ISBN-13 : OSU:32437123375301


The Journal Of Integral Equations And Applications

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Genre : Integral equations
Author :
Publisher :
Release : 2010
File : 688 Pages
ISBN-13 : UGA:32108041027916


Machine Learning In The Growth At Risk Context A Comparison Of Predictors

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Master's Thesis from the year 2022 in the subject Economics - Other, grade: 1,3, University of Frankfurt (Main), language: English, abstract: The Global Financial Crisis, starting in 2007, served as a reminder of the serious impact that imbalances originating in financial markets can have on economic growth. The aftermath of this economic shock with the ensuing recession continues to concern policymakers to this day. The subsequent period characterized by subdued growth and few but severe recessions gave rise to the importance of linkages between economic policy and risk management. The connection between this idea and the relevance of financial variables for analyzing growth risks is established by Adrian et al. (2019). They employ quantile regressions to examine the conditional distribution of future GDP growth and find that its left tail is exposed to substantially more volatility over time than the right tail. Moreover, they find that financial conditions for the US measured by the National Financial Conditions Index (NFCI) can serve as a relevant predictor of downside risk to conditional future economic growth. This thesis examines some machine-learning based variable selection methods that have been largely unexplored in the GaR context. The focus is on generating higher predictive power compared to the model by Adrian et al. (2019) rather than on analyzing economic relationships. The approaches described here are easy to apply and can help to automate the selection of variables for GaR estimation instead of having to manually choose relevant indicators. In detail, the LASSO method is used in the quantile regression context (Belloni and Chernozhukov 2011; Li and Zhu 2008), as well as the Adaptive (Wu and Liu 2009) and Relaxed LASSO (Meinshausen 2007), two of its modifications. In addition, the Elastic Net method is investigated as a compromise between Ridge and LASSO regression. To test the performance of these models, a backtesting exercise is conducted based on US data ranging from 1986 to 2019. The out-of-sample analysis is performed under the expanding and rolling window approach. For evaluation of the models, some of the backtesting tools used by Brownlees and Souza (2019) to perform a similar analysis for volatility models in the GaR context are utilized. In this regard, the following research question is formulated: Can the machine learning-based models improve the predictive power measured by the introduced backtesting tools for the investigated period compared to the quantile regression base model?

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Genre : Business & Economics
Author : Franz Lennart Wunderlich
Publisher : GRIN Verlag
Release : 2022-09-15
File : 112 Pages
ISBN-13 : 9783346724427


Ecological Techniques And Approaches To Vulnerable Environment

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This volume explores ecological techniques and approaches to vulnerable environments, looking at the hydrosphere-geosphere interaction. Topics include the impact of anthropogenic factors on climatic change; dam and water resource development and socio-cultural issues in resource managment.

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Genre : Business & Economics
Author : R. B. Singh
Publisher : Science Publishers
Release : 1998
File : 384 Pages
ISBN-13 : UOM:39015047594828


Multiple Regression Approach

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Genre : Mathematics
Author : Francis John Kelly
Publisher : Carbondale : Southern Illinois University Press
Release : 1969
File : 376 Pages
ISBN-13 : UCAL:B4451358


A Multiple Regression Approach To The Visual Involvement In The Localization Of Auditory Targets

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Genre : Auditory perception
Author : Bruce Bernard Platt
Publisher :
Release : 1975
File : 170 Pages
ISBN-13 : UCR:31210014768848


Machine Learning Ecml

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Genre : Induction (Logic)
Author :
Publisher :
Release : 1998
File : 448 Pages
ISBN-13 : UOM:39015039933240