Conditional Density Kernel Estimation Under Random Censorship for Functional Weak Dependence Data
Journal of MathematicsThe primary objective of this research is to investigate the asymptotic properties of the conditional density nonparametric estimator. The main areas of focus are the estimator’s consistency (with rates), including those involving censored data and quasi-
Hamza Daoudi +3 more
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Dependence Modeling in Count and Hybrid Insurance Data
MathematicsAccurate modeling of dependence in insurance data is essential for pricing, reserving, and capital allocation, particularly when claim frequencies and severities interact in complex ways.
Shiva Mehdipour Ghobadlou +2 more
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Nonparametric confidence bands in deconvolution density estimation [PDF]
Uniform confidence bands for densities f via nonparametric kernel estimates were first constructed by Bickel and Rosenblatt [Ann. Statist. 1, 1071.1095].
Bissantz, Nicolai +3 more
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Nonparametric Slope Estimators for Fixed-Effect Panel Data [PDF]
In panel data the interest is often in slope estimation while taking account of the unobserved cross sectional heterogeneity. This paper proposes two nonparametric slope estimation where the unobserved effect is treated as fixed across cross section. The
Kusum Mundra
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Efficient Estimation of an Additive Quantile Regression Model [PDF]
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the ...
Zerom, Dawit +2 more
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Nonparametric estimation of diffusion process: a closer look [PDF]
A Monte Carlo simulation is performed to investigate the finite sample properties of a nonparametric estimator, based on discretely sampled observations of continuous-time Ito diffusion process. Chapman and Pearson (2000) studies finite-sample properties
Orazio Di Miscia
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Asymptotic normality of locally modelled regression estimator for functional data
, 2016 We focus on the nonparametric regression of a scalar response on a functional explanatory variable. As analternative to the well-known Nadaraya-Watson estimator for regression function in this framework, thelocally modelled regression estimator performs ...
Zhou, Zhiyong,, Zhengyan, Lin,
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Nonparametric Estimation in the Cox Model
The Annals of Statistics, 1993 Using multivariate counting processes the paper derives nonparametric estimators of the relative risk in a generalized Cox regression model with covariates. The estimators are derived using the penalized partial likelihood approach. The paper considers the asymptotic convergence of these estimators in the Sobolev \(W^{bm}_ 2\) norm and obtains upper ...
openaire +3 more sources
Nonparametric Regression on Latent Covariates with an Application to Semiparametric GARCH-in-Mean Models [PDF]
We consider time series models in which the conditional mean of the response variable given the past depends on latent covariates. We assume that the covariates can be estimated consistently and use an iterative nonparametric kernel smoothing procedure ...
Christian Conrad, Enno Mammen
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A fully nonparametric estimator of the marginal survival function based on case-control clustered age-at-onset data. [PDF]
Biostatistics, 2017 Gorfine M, Bordo N, Hsu L.
europepmc +1 more source