Results 211 to 220 of about 19,465 (260)

Stratification-based instrumental variable analysis framework for nonlinear effect analysis. [PDF]

Biostatistics
Tian H, Patel A, Burgess S.
europepmc   +1 more source

Efficient Testing Using Surrogate Information. [PDF]

Biom J
Knowlton R, Parast L.
europepmc   +1 more source

M²NuFFT-A computationally efficient suboptimal power spectrum estimator for fast exploration of nonuniformly sampled time series. [PDF]

Digit Signal Process
Cui J, Brinkmann BH, Worrell GA.
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Aspects of nonparametric density estimation

, 1988
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Density Distillation for Fast Nonparametric Density Estimation

IEEE Transactions on Neural Networks and Learning Systems, 2023
Nonparametric density estimation has been extensively used in various application scenarios and theoretical models. However, the modeling of these powerful methods is inseparable from the sample data and comes at the cost of repeated and intensive kernel calculations, which makes their efficiency greatly affected by the sample scale, data dimension ...
Shifeng Chen, Bopeng Fang
exaly   +4 more sources

On nonparametric kernel density estimates

Biometrika, 1990
SUMMARY The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is the only admissible kernel. An analysis of kernel density estimates leads to two new methods of bias reduction. We also discuss a general method of improving kernel density estimates in the sense of having smaller mean squared error.
M. Samiuddin, G. M. El-Sayyad
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Developments in Nonparametric Density Estimation

International Statistical Review / Revue Internationale de Statistique, 1980
Summary The object of the present study is to summarize recent developments in nonparametric density estimation. The study covers the period of time from 1956 to 1978. Most of the important types of nonparametric density estimations are discussed. These include Parzen or kernel estimators, series estimators, penalized maximum likelihood estimators, and
Bean, Steven J., Tsokos, Chris P.
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Nonparametric estimates of probability densities

IEEE Transactions on Information Theory, 1975
A disadvantage of the kernel estimate of a probability density is that the degree of smoothing about the observation points is chosen without regard to the data. A modification of the kernel estimate is proposed that allows the data to play a role in this smoothing and, at the same time, retains the desirable features of the kernel estimate.
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On stochastic complexity and nonparametric density estimation

Biometrika, 1988
Let an unknown density f be supported within the rectangular prism \(R\subset R^ k\). R is partitioned into m congruent cells \(C_ i\), for \(1\leq i\leq m\). A histogram estimator of f is defined by the authors to take constant values on the cells, and so their prior is distributed over the class of all such densities, i.e. \(f(x)=mp_ i\), when \(x\in
Hall, Peter, Hannan, E. J.
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