Results 21 to 30 of about 3,774,080 (285)
Empirical Density Estimation for Interval Censored Data
Austrian Journal of Statistics, 2016 This paper is concerned with the nonparametric estimation of a density function when the data are incomplete due to interval censoring. The Nadaraya-Watson kernel density estimator is modified to allow description of such interval data.
Eugenia Stoimenova
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Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
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Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model
Mathematics, 2021 Estimation of probability density functions (pdf) is considered an essential part of statistical modelling. Heteroskedasticity and outliers are the problems that make data analysis harder. The Cauchy mixture model helps us to cover both of them.
Tomas Ruzgas +2 more
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Probability density estimation using data projection
Lietuvos Matematikos Rinkinys, 2009 Nonparametric estimation of multivariate multimodal probability density is analysed. The projection pursuit density estimator was proposed by J.H. Friedman.
Mindaugas Kavaliauskas
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Mathematics, 2022 Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE).
Jenny Farmer +2 more
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Kernel Density Estimators for Gaussian Mixture Models
Lithuanian Journal of Statistics, 2013 The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
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A kernel type nonparametric density estimator for decompounding
Bernoulli, 2007 Given a sample from a discretely observed compound Poisson process, we consider estimation of the density of the jump sizes. We propose a kernel type nonparametric density estimator and study its asymptotic properties. An order bound for the bias and an asymptotic expansion of the variance of the estimator are given.
van Es, A.J. +2 more
openaire +5 more sources
IEEE Access, 2019 In this paper, a nonparametric spatial-temporal self-exciting point process is proposed to model clustering features in emergency calls. Gaussian kernel density functions are considered.
Chenlong Li, Zhanjie Song, Xu Wang
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Targeted decrease of portal hepatic pressure gradient improves ascites control after TIPS
Hepatology, EarlyView., 2022 The river diagram demonstrates that after transjugular intrahepatic portosystemic shunt insertion (TIPS) the majority of patients without ascites and 50% of the patients with ascites detectable at ultrasound, show the best response in the long term follow‐up.
Alexander Queck +14 more
wiley +1 more source
Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation
IEEE Access, 2019 Wind energy is a kind of sustainable energy with strong uncertainty. With a large amount of wind power injected into the power grid, it will inevitably affect the security, stability and economic operation of the power grid.
Bowen Zhou +3 more
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