A Kernel-Based Nonparametric Multiple imputation method is proposed under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. We experimentally evaluate our approach, and demonstrate that our imputation performs better than the well-known NORM algorithm.

We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and dependent variable can be contemporaneously correlated. The asymptotic properties of the Nadaraya-Watson estimator are already examined in the literature. In this paper, we consider nonparametric least absolute deviation (LAD) regression and derive the asymptotic distributions of the local constant and ...

We introduce a new truncation approach to extend earlier methods for proving consistency in nonparametric Bayesian regression problems to non-compact state spaces. We illustrate the approach by proving posterior consistency for a nonparametric Poisson regression model. The key step is separating points in the parameter space by constructing hypothesis tests with suitably small error rates; we d...

In this paper we develop a semi-parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of timeof-day and type-of-day via nonparametric estimation. In addition, an ARIMA model is used t...

We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references ...

The ROC (Receiver Operating Chracteristic) curves are frequently used for different diagnostic purposes. There are several different approaches how to find the suitable estimate of the ROC curve in binormal model. The effective methods which can be used when the sample sizes are small are still very demanded in different applications. In the paper the binormal model is assumed and the parametri...

A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show that quantiles estimated by nonparametric method using these techniques have small root mean squ...

Learning a common latent representation can capture the relationships and share statistic strength among multiple tasks. To automatically resolve the unknown dimensionality of the latent representation, nonparametric Bayesian methods have been successfully developed with a generative process describing the observed data. In this paper, we present a discriminative approach to learning nonparamet...

We propose a quantile-based nonparametric approach to inference on the probability density function (PDF) of the private values in …rst-price sealedbid auctions with independent private values. Our method of inference is based on a fully nonparametric kernel-based estimator of the quantiles and PDF of observable bids. Our estimator attains the optimal rate of Guerre, Perrigne, and Vuong (2000),...