Nonparametric adaptive estimation for grouped data
نویسندگان
چکیده
منابع مشابه
Nonparametric adaptive estimation for grouped data
The aim of this paper is to estimate the density f of a random variable X when one has access to independent observations of the sum of K ≥ 2 independent copies of X . We provide a constructive estimator based on a suitable definition of the logarithm of the empirical characteristic function. We propose a new strategy for the data driven choice of the cut-off parameter. The adaptive estimator i...
متن کاملNonparametric Regression Estimation under Kernel Polynomial Model for Unstructured Data
The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framewo...
متن کاملIsotonic estimation for grouped data
A non-parametric estimator of a non-increasing density is found in a class of piecewise linear functions when the data consist only of counts. An EM-Algorithm for computing the estimator is developed, and the iterates in the algorithm are shown to converge to the maximum likelihood estimator. Potential applications to distance sampling models are described and illustrated with a numerical examp...
متن کاملNonparametric estimation for dependent data
Nonparametric estimation for dependent observations has a long history in statistics. Rosenblatt [42] first studied density estimation for dependent data. Since then several authors have considered nonparametric estimation under various assumptions (notable early articles include Robinson [39] and Hart [29]). For example, Hall and Hart [25], Giraitis et al. [22], Mielniczuk [34] and Estevas and...
متن کاملAdaptive Drift Estimation for Nonparametric Diffusion Model
We consider a nonparametric diffusion process whose drift and diffusion coefficients are nonparametric functions of the state variable. The goal is to estimate the unknown drift coefficient. We apply a locally linear smoother with a data-driven bandwidth choice. The procedure is fully adaptive and nearly optimal up to a log log factor. The results about the quality of estimation are nonasymptot...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Planning and Inference
سال: 2017
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2016.10.002