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BOOK EXCERPT:
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.
Product Details :
Genre |
: Mathematics |
Author |
: Richard A. Berk |
Publisher |
: Springer Science & Business Media |
Release |
: 2008-06-14 |
File |
: 373 Pages |
ISBN-13 |
: 9780387775012 |
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Product Details :
Genre |
: Regression analysis |
Author |
: Richard A. Berk |
Publisher |
: |
Release |
: 2018 |
File |
: 358 Pages |
ISBN-13 |
: 7519244636 |
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Product Details :
Genre |
: Regression analysis |
Author |
: Richard A. Berk |
Publisher |
: |
Release |
: 2024 |
File |
: 0 Pages |
ISBN-13 |
: 7523211321 |
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Product Details :
Genre |
: Industrial relations |
Author |
: |
Publisher |
: |
Release |
: 2014 |
File |
: 576 Pages |
ISBN-13 |
: OSU:32437123375301 |
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Product Details :
Genre |
: Integral equations |
Author |
: |
Publisher |
: |
Release |
: 2010 |
File |
: 688 Pages |
ISBN-13 |
: UGA:32108041027916 |
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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?
Product Details :
Genre |
: Business & Economics |
Author |
: Franz Lennart Wunderlich |
Publisher |
: GRIN Verlag |
Release |
: 2022-09-15 |
File |
: 112 Pages |
ISBN-13 |
: 9783346724427 |
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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.
Product Details :
Genre |
: Business & Economics |
Author |
: R. B. Singh |
Publisher |
: Science Publishers |
Release |
: 1998 |
File |
: 384 Pages |
ISBN-13 |
: UOM:39015047594828 |
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Product Details :
Genre |
: Mathematics |
Author |
: Francis John Kelly |
Publisher |
: Carbondale : Southern Illinois University Press |
Release |
: 1969 |
File |
: 376 Pages |
ISBN-13 |
: UCAL:B4451358 |
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Product Details :
Genre |
: Auditory perception |
Author |
: Bruce Bernard Platt |
Publisher |
: |
Release |
: 1975 |
File |
: 170 Pages |
ISBN-13 |
: UCR:31210014768848 |
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BOOK EXCERPT:
Product Details :
Genre |
: Induction (Logic) |
Author |
: |
Publisher |
: |
Release |
: 1998 |
File |
: 448 Pages |
ISBN-13 |
: UOM:39015039933240 |