Results 91 to 100 of about 25,321,415 (248)
Comparing Single Vs. Hybrid models in Time Series Forecasting [PDF]
المجلة العلمية للدراسات والبحوث المالية والتجارية:The research aims to forecast time series relying on individual models SVR, ARIMA, and the hybrid model "ARIMA-SVR" through different integration methods applied to global oil price data from January 2004 to December 2023, comprising monthly data with ...
مها توفيق +1 more
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Additive and Generalized Additive Models
, 2006 This paper is the attempt to summarize the state of art in additive and generalized additive models (GAM). The emphasis is on approaches and numerical procedures which have emerged since the monograph of Hastie and Tibshirani (1990) although reconsidering certain aspects of their work.
Schimek, Michael G., Turlach, Berwin A.
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Group Sparse Additive Models [PDF]
Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning, 2012 We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or address the problem in the non ...
Junming Yin, Xi Chen 0010, Eric P. Xing
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Penalized additive regression for space-time data: a Bayesian perspective [PDF]
, 2003 We propose extensions of penalized spline generalized additive models for analysing space-time regression data and study them from a Bayesian perspective.
Stefan Lang +5 more
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Non-Standard Semiparametric Regression via BRugs
Journal of Statistical Software, 2010 We provide several illustrations of Bayesian semiparametric regression analyses in the BRugs package. BRugs facilitates use of the BUGS inference engine from the R computing environment and allows analyses to be managed using scripts.
Jennifer K. Marley, Matthew P. Wand
doaj
Generalized Additive Models for Location Scale and Shape (GAMLSS) in R
, 2007 GAMLSS is a general framework for fitting regression type models where the distribution of the response variable does not have to belong to the exponential family and includes highly skew and kurtotic continuous and discrete distribution.
D. Stasinopoulos, R. Rigby
semanticscholar +1 more source
Error Variance Estimation in Ultrahigh-Dimensional Additive Models
Journal of the American Statistical Association, 2017 Error variance estimation plays an important role in statistical inference for high-dimensional regression models. This article concerns with error variance estimation in high-dimensional sparse additive model.
Zhao Chen, Jianqing Fan, Runze Li
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Bayesian Regularisation in Structured Additive Regression Models for Survival Data [PDF]
, 2008 During recent years, penalized likelihood approaches have attracted a lot of interest both in the area of semiparametric regression and for the regularization of high-dimensional regression models.
Konrath, Susanne +2 more
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Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models
, 2017 In Pay-As-You-Drive (PAYD) automobile insurance, the premium is fixed based on the distance traveled, while in usage-based insurance (UBI) the driving patterns of the policyholder are also considered. In those schemes, drivers who drive more pay a higher
J. Boucher +2 more
semanticscholar +1 more source
Phantom Epistasis in Genomic Selection: On the Predictive Ability of Epistatic Models
G3: Genes, Genomes, Genetics, 2020 Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic.
Matías F. Schrauf +7 more
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