Results 31 to 40 of about 12,193,766 (206)
Local Dimensionality Reduction for Non-Parametric Regression [PDF]
Neural Processing Letters, 2009 Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many local models need to be maintained simultaneously.
Heiko Hoffmann +2 more
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Testing the Equality of Two Parametric Quantile Regression Curves : The Application for Comparing Two Data Sets [PDF]
, 2016 This study aims to compare the different between two data sets that having the relationship between the dependent and independent variables at each quantile using testing the equality of two parametric quantile regression functions, the conditional ...
Tonggumnead, Unchalee; Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, 39 Moo1, Rangsit-Nakhonnayok Rd. Klong6, Thanyaburi, Pathum Thani 12110Thailand
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Energies, 2021 Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities.
Takuji Matsumoto, Yuji Yamada
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Plants, 2023 Lentil yield is a complicated quantitative trait; it is significantly influenced by the environment. It is crucial for improving human health and nutritional security in the country as well as for a sustainable agricultural system. The study was laid out
Md. Amir Hossain +8 more
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JOIV: International Journal on Informatics VisualizationResearchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors ...
Amjed Mohammed Sadek, Lekaa Ali Mohammed
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Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression
UniverseThe current accelerated expansion of the Universe remains one of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application
José de Jesús Velázquez +3 more
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On the pitfalls of Gaussian likelihood scoring for causal discovery
Journal of Causal Inference, 2023 We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising
Schultheiss Christoph, Bühlmann Peter
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Human Genomics, 2003 In the search for genetic determinants of complex disease, two approaches to association analysis are most often employed, testing single loci or testing a small group of loci jointly via haplotypes for their relationship to disease status.
Zhang Xiaohua +3 more
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, 2016 In this article, we propose a flexible parametric (FP) approach for adjusting for covariate measurement errors in regression that can accommodate replicated measurements on the surrogate (mismeasured) version of the unobserved true covariate on all the ...
Hossain, Shahadut +2 more
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Beta-Stacy survival regression models [PDF]
, 2007 This paper introduces a class of survival models for discrete time-to-event data with random right censoring. Flexible distributions for the survival times are constructed by modelling the random survival functions as discrete-time beta-Stacy processes (
Muliere, Pietro, Rigat, Fabio
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