Results 21 to 30 of about 229,123 (176)
Journal of Statistical Software, 2014 Convex optimization now plays an essential role in many facets of statistics. We briefly survey some recent developments and describe some implementations of these methods in R .
Roger Koenker, Ivan Mizera
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Stats, 2023 To address the difficult problem of the multi-step-ahead prediction of nonparametric autoregressions, we consider a forward bootstrap approach. Employing a local constant estimator, we can analyze a general type of nonparametric time-series model and ...
Dimitris N. Politis, Kejin Wu
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Bayesian nonparametric subspace estimation [PDF]
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 Principal component analysis is a widely used technique to perform dimension reduction. However, selecting a finite number of significant components is essential and remains a crucial issue. Only few attempts have proposed a probabilistic approach to adaptively select this number. This paper introduces a Bayesian nonparametric model to jointly estimate
Elvira, Clément +2 more
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Estimation and Inference for Spatio-Temporal Single-Index Models
Mathematics, 2023 To better fit the actual data, this paper will consider both spatio-temporal correlation and heterogeneity to build the model. In order to overcome the “curse of dimensionality” problem in the nonparametric method, we improve the estimation method of the
Hongxia Wang +3 more
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Nonparametric Range-Based Double Smoothing Spot Volatility Estimation for Diffusion Models
Complexity, 2020 We consider nonparametric spot volatility estimation for diffusion models with discrete high frequency observations. Our estimator is carried out in two steps.
Jingwei Cai
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Finite-Sample Bounds on the Accuracy of Plug-In Estimators of Fisher Information
Entropy, 2021 Finite-sample bounds on the accuracy of Bhattacharya’s plug-in estimator for Fisher information are derived. These bounds are further improved by introducing a clipping step that allows for better control over the score function.
Wei Cao +3 more
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Entropy, 2008 At the heart of many ICA techniques is a nonparametric estimate of an information measure, usually via nonparametric density estimation, for example, kernel density estimation.
Julian Sorensen
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Adaptive Reduction of Curse of Dimensionality in Nonparametric Instrumental Variable Estimation
MathematicsNonparametric estimation of instrumental variable treatment effects typically builds on various nonparametric identification results. However, these estimators often face challenges from the curse of dimensionality in practice, as multi-dimensional ...
Ming-Yueh Huang, Kwun Chuen Gary Chan
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Energy Reports, 2022 In today’s increasingly serious world energy crisis, Renewable energy such as wind energy has gradually penetrated into life. Aiming at the uncertainty of wind power and the need of a mass of sample data in nonparametric kernel density estimation, a wind
Kai Zhang +6 more
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Semi-Nonparametric Maximum Likelihood Estimation [PDF]
Econometrica, 1987 The density of Hermite forms: \[ h(u)=P^ 2_ k(u-\tau)\Phi^ 2(u| \tau,diag(\gamma)) \] where \(P_ k\) is a polynomial of degree K and \(\Phi\) is the density function of the multivariate normal distribution is shown to be capable of approximating any density arbitrarily closely subject to minimal qualifications relating to compactness, denseness ...
Gallant, A Ronald, Nychka, Douglas W
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