Corpus ID: 229332290

Nonparametric Estimation of Repeated Densities with Heterogeneous Sample Sizes

@inproceedings{Qiu2020NonparametricEO,
  title={Nonparametric Estimation of Repeated Densities with Heterogeneous Sample Sizes},
  author={Jiaming Qiu and Xiongtao Dai and Zhengyuan Zhu},
  year={2020}
}
We consider the estimation of densities in multiple subpopulations, where the available sample size in each subpopulation greatly varies. For example, in epidemiology, different diseases may share similar pathogenic mechanism but differ in their prevalence. Without specifying a parametric form, our proposed approach pools information from the population and estimate the density in each subpopulation in a data-driven fashion. Low-dimensional approximating density families in the form of… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 45 REFERENCES
Inference for Density Families Using Functional Principal Component Analysis
We consider t = 1,...,T samples of iid observations {X1t,…,Xntt} from unknown population densities {ft}. To characterize differences and similarities of {ft}, we assume their expansions into theExpand
FUNCTIONAL DATA ANALYSIS FOR POINT PROCESSES WITH RARE EVENTS
In various applications one encounters samples of objects, where each object consists of a small number of repeated event times observed over a fixed time interval. For such rare event data there areExpand
Functional Data Analysis for Sparse Longitudinal Data
We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where theExpand
A study of logspline density estimation
A method of estimating an unknown density function @? based on sample data is studied. Our approach is to use maximum likelihood etimation to estimate log(@?) by a function s from a space of cubicExpand
Additive Functional Regression for Densities as Responses
Abstract We propose and investigate additive density regression, a novel additive functional regression model for situations where the responses are random distributions that can be viewed as randomExpand
Dimensionality reduction when data are density functions
  • P. Delicado
  • Mathematics, Computer Science
  • Comput. Stat. Data Anal.
  • 2011
TLDR
Compared dimensionality reduction methods for functional principal components analysis with or without a previous data transformation, and multidimensional scaling for different inter-density distances, one of them taking into account the compositional nature of density functions. Expand
Semiparametric Exponential Families for Heavy-Tailed Data
We propose a semiparametric method for fitting the tail of a heavy-tailed population given a relatively small sample from that population and a larger sample from a related background population. WeExpand
Issues in the statistical analysis of small area health data.
TLDR
A general framework for the statistical analysis of small area studies will be considered and the use of errors-in-variables modelling in small area analyses will be discussed. Expand
A reliable data-based bandwidth selection method for kernel density estimation
We present a new method for data-based selection of the bandwidth in kernel density estimation which has excellent properties. It improves on a recent procedure of Park and Marron (which itself is aExpand
Statistical Inference on the Hilbert Sphere with Application to Random Densities
The infinite-dimensional Hilbert sphere S∞ has been widely employed to model density functions and shapes, extending the finite-dimensional counterpart. We consider the Fréchet mean as an intrinsicExpand
...
1
2
3
4
5
...