Results 211 to 220 of about 6,398 (252)

Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be. [PDF]

Epidemiology
Zeng Z, Bodnar LM, Naimi AI.
europepmc   +1 more source

Nonparametric Density Estimation for Data Scattered on Irregular Spatial Domains: A Likelihood-Based Approach Using Bivariate Penalized Spline Smoothing. [PDF]

J Nonparametr Stat
Das K, Yu S, Wang G, Wang L.
europepmc   +1 more source

Modified partial least square structural equation model with multivariate adaptive regression spline: Parameter estimation technique and applications. [PDF]

MethodsX
Dukalang HH, Otok BW, Purhadi.
europepmc   +1 more source

Coarse-Grained Hawkes Processes. [PDF]

Entropy (Basel)
Koyama S.
europepmc   +1 more source

Covariate selection strategies and estimands - a review of current practice of risk factor analysis from a causal perspective. [PDF]

BMC Med Res Methodol
Reinhammar R, Waernbaum I.
europepmc   +1 more source

Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach. [PDF]

Entropy (Basel)
Chen R, Chen Z.
europepmc   +1 more source

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Nonparametric multivariate density estimation: a comparative study

IEEE Transactions on Signal Processing, 1994
The paper algorithmically and empirically studies two major types of nonparametric multivariate density estimation techniques, where no assumption is made about the data being drawn from any of known parametric families of distribution. The first type is the popular kernel method (and several of its variants) which uses locally tuned radial basis (e.g.,
Jenq-Neng Hwang, Shyh-Rong Lay, Alan F. Lippman   +2 more
openaire   +1 more source

Nonparametric multivariate density estimation using mixtures

Statistics and Computing, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xuxu Wang, Yong Wang 0049
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Nearly Nonparametric Multivariate Density Estimates That Incorporate Marginal Parametric Density Information

The American Statistician, 2003
When data analysts have multivariate data, often they have partial knowledge about the form of the marginal densities, but frequently they have little information about the bivariate and higher dimensional densities. This article provides nonparametric estimators that nearly equal the MLE estimates for the marginal densities while being close to the ...
Clifford Spiegelman, Eun Sug Park
openaire   +3 more sources

On nonparametric density estimation for multivariate linear long-memory processes

Communications in Statistics - Theory and Methods, 2018
We consider nonparametric estimation of the density function and its derivatives for multivariate linear processes with long-range dependence.
Jan Beran, Klaus Telkmann
openaire   +1 more source