Results 41 to 50 of about 205,425 (288)
lpdensity: Local Polynomial Density Estimation and Inference
Journal of Statistical Software, 2022 Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known ...
Matias D. Cattaneo +2 more
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Approximate Interpolation with Applications to Selecting Smoothing Parameters
Numerische Mathematik, 2005 An approximation problem can be shortly stated as follows: for a finite set \(X\) of points situated in a bounded set \(\Omega\) and a corresponding data values of an unknown function \(f \in C(\Omega)\), a function \(s_{f,X} \in C(\Omega)\) to produce a good approximation is required.
Wendland, Holger, Rieger, C.
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Model selection and parameter estimation using the iterative smoothing method [PDF]
Journal of Cosmology and Astroparticle Physics, 2021 Abstract We compute the distribution of likelihoods from the non-parametric iterative smoothing method over a set of mock Pantheon-like type Ia supernova datasets. We use this likelihood distribution to test whether typical dark energy models are consistent with the data and to perform parameter estimation.
Koo, Hanwool +3 more
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Determining an effective short term COVID-19 prediction model in ASEAN countries
Scientific Reports, 2022 The challenge of accurately short-term forecasting demand is due to model selection and the nature of data trends. In this study, the prediction model was determined based on data patterns (trend data without seasonality) and the accuracy of prediction ...
Omar Sharif +2 more
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Empirical Functionals and Efficient Smoothing Parameter Selection
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1992 SUMMARY A striking feature of curve estimation is that the smoothing parameter ĥ 0, which minimizes the squared error of a kernel or smoothing spline estimator, is very difficult to estimate. This is manifest both in slow rates of convergence and in high variability of standard methods such as cross-validation.
Peter Hall, Iain Johnstone
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Testing Symmetry of Unknown Densities via Smoothing with the Generalized Gamma Kernels
Econometrics, 2016 This paper improves a kernel-smoothed test of symmetry through combining it with a new class of asymmetric kernels called the generalized gamma kernels. It is demonstrated that the improved test statistic has a normal limit under the null of symmetry and
Masayuki Hirukawa, Mari Sakudo
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Multiple smoothing parameters selection in additive regression quantiles
Statistical Modelling, 2020 We propose an iterative algorithm to select the smoothing parameters in additive quantile regression, wherein the functional forms of the covariate effects are unspecified and expressed via B-spline bases with difference penalties on the spline coefficients. The proposed algorithm relies on viewing the penalized coefficients as random effects from the
Muggeo, Vito M.R. +4 more
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Revista Iberoamericana de Automática e Informática Industrial RIAI, 2017 In this work, a methodology is proposed for the improvement of the parameter estimation effciency of a non-linear manoeuvring model of a torpedo shaped unmanned underwater vehicle.
Elías Revestido Herrero +3 more
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New Bandwidth Selection for Kernel Quantile Estimators
Journal of Probability and Statistics, 2012 We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a ...
Ali Al-Kenani, Keming Yu
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Proceedings, 2018 Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed ...
Inés Barbeito, Ricardo Cao
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