We propose estimating equations whose unknown parameters are the values taken by a circular density and its derivatives at a point. Specifically, we solve equations which relate local versions of population trigonometric moments with their sample counterparts. Major advantages of our approach are: higher order bias without asymptotic variance inflation, closed form for the estimators, and absence of numerical tasks. We also investigate situations where the observed data are dependent. Theoretical results along with simulation experiments are provided
Nonparametric density and regression estimation methods for circular data are included in the R pack...
Nonparametric density and regression estimation methods for circular data are included in the R pack...
We discuss nonparametric estimation of conditional quantiles of a circular distribution when the con...
We propose estimating equations whose unknown parameters are the values taken by a circular density ...
The conditional density offers the most informative summary of the relationship between explanatory ...
We consider the problem of nonparametrically estimating a circular density from data contaminated by...
This paper introduces a new non-parametric approach to the modeling of circular data, based on the ...
AbstractUntil now the problem of estimating circular densities when data are observed with errors ha...
Kernel density estimation for multivariate, circular data has been formulated only when the sample s...
Until now the problem of estimating circular densities when data are observed with errors has been m...
The circular kernel density estimator, with the wrapped Cauchy kernel, is derived from the empirical...
I few years ago, while I was working on kernel based density estimation on compact support distribut...
This paper aims to introduce an estimation algorithm for the joint densityof a circular-circular ran...
The statistical analysis of circular, multivariate circular, and spherical data is very important in...
Nearest neighbour methods traditionally used to estimate density of a sessile biological population ...
Nonparametric density and regression estimation methods for circular data are included in the R pack...
Nonparametric density and regression estimation methods for circular data are included in the R pack...
We discuss nonparametric estimation of conditional quantiles of a circular distribution when the con...
We propose estimating equations whose unknown parameters are the values taken by a circular density ...
The conditional density offers the most informative summary of the relationship between explanatory ...
We consider the problem of nonparametrically estimating a circular density from data contaminated by...
This paper introduces a new non-parametric approach to the modeling of circular data, based on the ...
AbstractUntil now the problem of estimating circular densities when data are observed with errors ha...
Kernel density estimation for multivariate, circular data has been formulated only when the sample s...
Until now the problem of estimating circular densities when data are observed with errors has been m...
The circular kernel density estimator, with the wrapped Cauchy kernel, is derived from the empirical...
I few years ago, while I was working on kernel based density estimation on compact support distribut...
This paper aims to introduce an estimation algorithm for the joint densityof a circular-circular ran...
The statistical analysis of circular, multivariate circular, and spherical data is very important in...
Nearest neighbour methods traditionally used to estimate density of a sessile biological population ...
Nonparametric density and regression estimation methods for circular data are included in the R pack...
Nonparametric density and regression estimation methods for circular data are included in the R pack...
We discuss nonparametric estimation of conditional quantiles of a circular distribution when the con...