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Two General Methods for Population Pharmacokinetic Modeling: Non-Parametric Adaptive Grid and Non-Parametric Bayesian
Population pharmacokinetic modeling non-parametric maximum likelihood Bayesian Stick-breaking Pmetrics RJags
2013/5/2
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian...
A non-parametric mixture model for topic modeling over time
non-parametric mixture model topic modeling over time
2012/9/17
A single, stationary topic model such as la-tent Dirichlet allocation is inappropriate for modeling corpora that span long time peri-ods, as the popularity of topics is likely to change over time. A n...
Gaussian Oracle Inequalities for Structured Selection in Non-Parametric Cox Model
Gaussian Oracle Inequalities Structured Selection Non-Parametric Cox Model
2012/9/19
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard乫s model. Due to high impact of sequencing data, carrying genet...
Non-parametric change-point detection using string matching algorithms
School of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK
2011/7/7
Given the output of a data source taking values in a finite alphabet, we wish to detect change-points, that is times when the statistical properties of the source change.
Asymptotic minimax risk of predictive density estimation for non-parametric regression
asymptotic minimax risk convergence rate non-parametric regression
2010/10/19
We consider the problem of estimating the predictive density of future observations from a non-parametric regression model. The density estimators are evaluated under Kullback--Leibler divergence and ...
Non-Parametric Volatility Estimation in Continuous Time
Non-Parametric Volatility Estimation Continuous Time
2009/9/17
Non-Parametric Volatility Estimation in Continuous Time。
Selection of variables and dimension reduction in high-dimensional non-parametric regression
dimension reduction high dimension LASSO
2009/9/16
We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on...
Quantifying the cost of simultaneous non-parametric approximation of several samples
Modality non-parametric regression penalization regularization total variation
2009/9/16
We consider the standard non-parametric regression model with Gaussian errors but where the data consist of different samples. The question to be answered is whether the samples can be adequately repr...
Exact Non-Parametric Bayesian Inference on Infinite Trees
Bayesian density estimation exact linear time algorithm non-parametric inference adaptive infinite tree Polya tree
2010/3/19
Given i.i.d. data from an unknown distribution, we consider the problem
of predicting future items. An adaptive way to estimate the probability density
is to recursively subdivide the domain to an a...
A NON-PARAMETRIC TEST FOR NON-INDEPENDENT NOISES AGAINST A BILINEAR DEPENDENCE
time series asymptotic separation bilinear models test
2009/2/26
A new methodology, based on the asymptotic separation of probability laws, was introduced by Gon»calves, Jacob and Mendes-Lopes (2000) in the development of the statistical inference of bilinear...