Results 21 to 30 of about 76,031 (307)
A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko +2 more
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Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models
Revstat Statistical Journal, 2021 This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz +2 more
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Journal of Open Innovation: Technology, Market and Complexity, 2023 This research concentrates on using neural networks in the modelling and prediction of macroeconomic variables in specific. Macroeconomic predictors are particularly interested in neural networks because of their capacity to predict any linear or non ...
Rabia Sabri +5 more
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Resistant Nonparametric Smoothing with S-PLUS
Journal of Statistical Software, 2004 In this paper we introduce and illustrate the use of an S-PLUS set of functions to fit M-type smoothing splines with the smoothing parameter chosen by a robust criterion (either a robust version of cross-validation or a robust version of Mallows's Cp ...
Eva Cantoni
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Penalized spline estimator with multi smoothing parameters in bi-response multi-predictor nonparametric regression model for longitudinal data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2020 Penalized spline estimators that depend on a smoothing parameter is one type of estimator used in the estimation regression curve in nonparametric regression.
Anna Islamiyati, Fatmawati, Nur Chamidah
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Stats, 2020 In a functional linear model (FLM) with scalar response, the parameter curve quantifies the relationship between a functional explanatory variable and a scalar response.
Eduardo L. Montoya
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Estimation of the Smoothing Parameters in the HPMV Filter
Annals of the Alexandru Ioan Cuza University - Mathematics, 2011 We suggest an optimality criterion, for choosing the best smoothing parameters for an extension of the so-called Hodrick-Prescott Multivariate (HPMV) filter. We show that this criterion admits a whole set of optimal smoothing parameters, to which belong the widely used noise-to-signal ratios.
Rahmania, Nadji +2 more
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Remote Sensing, 2020 Accurate evaluation of start of season (SOS) changes is essential to assess the ecosystem’s response to climate change. Smoothing method is an understudied factor that can lead to great uncertainties in SOS extraction, and the applicable situation for ...
Nan Li +5 more
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Dynamic smoothness parameter for fast gradient methods [PDF]
Optimization Letters, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
FRANGIONI, ANTONIO +2 more
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Unsupervised estimation of dirichlet smoothing parameters
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2010 A standard approach for determining a Dirichlet smoothing parameter is to choose a value which maximizes a retrieval performance metric using training data consisting of queries and relevance judgments. There are, however, situations where training data does not exist or the queries and relevance judgments do not reflect typical user information needs ...
Jangwon Seo, W. Bruce Croft
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