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Smooth plug-in inverse estimators in the current status continuous mark model
asymptotic distribution bivariate kernel estimation continuous mark variable consistency current status data plug-in estimation
2011/3/21
We consider the problem of estimating the joint distribution function of the event time and a continuous mark variable when the event time is subject to interval censoring case 1 and the continuous ma...
New estimators of the Pickands dependence function and a test for extreme-value dependence
Pickands dependence function a test for extreme-value dependence
2011/3/18
We propose a new class of estimators for Pickands dependence function which is based on the concept of minimum distance estimation. An explicit integral representation of the function A^*(t), which mi...
A general purpose variance reduction technique for Markov chain Monte Carlo estimators based on the zero-variance principle introduced in the physics literature by Assaraf and Caffarel (1999, 2003), i...
The Importance of Scale for Spatial-Confounding Bias and Precision of Spatial Regression Estimators
Epidemiology, identifiability, mixed model,penalized likelihood random effects spatial correlation splines
2010/11/9
Residuals in regression models are often spatially correlated.Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants.
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
unified framework high-dimensional analysis $M$-estimators decomposable regularizers
2010/10/19
High-dimensional statistical inference deals with models in which the the number of parameters $p$ is comparable to or larger than the sample size $n$. Since it is usually impossible to obtain consist...
Asymptotic distribution of conical-hull estimators of directional edges
Asymptotic distribution conical-hull estimators directional edges
2010/10/14
Nonparametric data envelopment analysis (DEA) estimators have been widely applied in analysis of productive efficiency. Typically they are defined in terms of convex-hulls of the observed combinations...
A universal procedure for aggregating estimators
universal procedure aggregating estimators
2010/4/28
Typically aggregation procedures involve splitting the sample into two
sub-samples: the candidate estimators are constructed on the basis of the
first sub-sample, while the second subsample is used ...
Maxiset in sup-norm for kernel estimators。
A General Family of Estimators for Estimating Population Mean Using Known Value of Some Population Parameter(s)
Auxiliary information general family of estimators bias mean-squared error population parameter(s)
2010/4/26
A general family of estimators for estimating the population mean of the variable
under study, which make use of known value of certain population parameter(s), is proposed.
Under Simple Random Samp...
Efficient estimators：the use of neural networks to construct pseudo panels
pseudo-panels Kohonen map measurement error AIDS model
2010/4/26
Pseudo panels constituted with repeated cross-sections are good substitutes to true panel data. But individuals grouped in a cohort are not the same for successive periods, and it results in a measure...
Kernel methods and minimum contrast estimators for empirical deconvolution
bandwidth inverse problems kernel estimators local linearmethods local polynomial methods minimum contrast methods
2010/3/11
We survey classical kernel methods for providing nonparametric solutions
to problems involving measurement error. In particular we outline
kernel-basedmethodology in this setting, and discuss its ba...
Product-limit estimators of the gap time distribution of a renewal process under different sampling patterns
Kaplan-Meier estimator Cox-Vardi estimator Laslett's line segment problem nonparametric maximum likelihood Markov process
2010/3/11
Nonparametric estimation of the gap time distribution in a simple re-
newal process may be considered a problem in survival analysis under
particular sampling frames corresponding to how the renewal...
Efficient Bayesian Learning in Social Networks with Gaussian Estimators
Efficient Bayesian Learning Social Networks Gaussian Estimators
2010/3/10
We propose a simple and efficient Bayesian model of iterative learning on social networks.
This model is efficient in two senses: the process both results in an optimal belief, and can
be carried ou...
On some Bayesian nonparametric estimators for species richness under two-parameter Poisson-Dirichlet priors
Bayesian nonparametric estimators species richness two-parameter Poisson-Dirichlet priors
2010/3/11
We present an alternative approach to the Bayesian nonparametric analysis of conditional
species richness under two-parameter Poisson Dirichlet priors. We rely on a known characteri-
zation by delet...
Minimax properties of beta kernel density estimators
Beta Kernel Density Minimax estimation
2010/3/9
In this paper, we are interested in the study of beta kernel estimators from
an asymptotic minimax point of view. It is well known that beta kernel estimators
are—on the contrary of classical kernel...