With the aim of mitigating the possible problem of negativity in the estimation of the conditional density function, we introduce a so-called re-weighted Nadaraya-Watson (RNW) estimator. The proposed RNW estimator is constructed by a slight modification of the well-known Nadaraya-Watson smoother. With a detailed asymptotic analysis, we demonstrate that the RNW smoother preserves the superior large-sample bias property of the local linear smoother of the conditional density recently proposed in the literature. As a matter of independent statistical interest, the limit distribution of the RNW estimator is also derived
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
The present paper is focused on non-parametric estimation of conditional density. Conditional densit...
With the aim of mitigating the possible problem of negativity in the estimation of the conditional d...
The conditional probability density function plays an important role in statistics. It describes the...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
International audienceThis paper introduces a computationally tractable density estimator that has t...
Conditional density estimation is a longstanding and challenging problem in statistical theory, and ...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
The present paper is focused on non-parametric estimation of conditional density. Conditional densit...
With the aim of mitigating the possible problem of negativity in the estimation of the conditional d...
The conditional probability density function plays an important role in statistics. It describes the...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
International audienceThis paper introduces a computationally tractable density estimator that has t...
Conditional density estimation is a longstanding and challenging problem in statistical theory, and ...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
The present paper is focused on non-parametric estimation of conditional density. Conditional densit...