A new kernel density estimator for length biased data which derives from smoothing the nonparametric maximum likelihood estimator is proposed and investigated. It has various advantages over an alternative method suggested by Bhattacharyya, Franklin & Richardson (1988): it is necessarily a probability density, it is particularly better behaved near zero, it has better asymptotic mean integrated squared error properties and it is more readily extendable to related problems such as density derivative estimation
Length biased sampling, as a special case of general biased sampling, occurs naturally in many stati...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Length biased sampling, as a special case of general biased sampling, occurs naturally in many stati...
This paper proposes and investigates Fourier series estimators for length biased data. Specifically,...
In this note non-parametric estimates of the length-biased probability density function and related ...
In this note non-parametric estimates of the length-biased probability density function and related ...
In this note non-parametric estimates of the length-biased probability density function and related ...
This article considers smooth density estimation based on length biased data that involves a random ...
Kernel density estimators have been studied in great detail. In this note a new family of kernels, d...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
There are various methods for estimating a density. A group of methods which estimate the density as...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
Length biased sampling, as a special case of general biased sampling, occurs naturally in many stati...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Length biased sampling, as a special case of general biased sampling, occurs naturally in many stati...
This paper proposes and investigates Fourier series estimators for length biased data. Specifically,...
In this note non-parametric estimates of the length-biased probability density function and related ...
In this note non-parametric estimates of the length-biased probability density function and related ...
In this note non-parametric estimates of the length-biased probability density function and related ...
This article considers smooth density estimation based on length biased data that involves a random ...
Kernel density estimators have been studied in great detail. In this note a new family of kernels, d...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
There are various methods for estimating a density. A group of methods which estimate the density as...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
Length biased sampling, as a special case of general biased sampling, occurs naturally in many stati...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...