The purpose of this paper is to provide some asymptotic results for nonparametric estimator of the Lorenz curve and Lorenz process for the case in which data are assumed to be strong mixing subject to random left truncation. First, we show that nonparametric estimator of the Lorenz curve is uniformly strongly consistent for the associated Lorenz curve. Also, a strong Gaussian approximation for ...

Abstract We propose a variational inference-based framework for training Gaussian process regression model subject to censored observational data. Data censoring is typical problem encountered during the data gathering procedure and requires specialized techniques perform inference since resulting probabilistic models are typically analytically intractable. In this article we exploit sparse ind...

Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in absence a prior/structure on templates. In data-scarce or low signal-to-noise ratio (SNR) regimes, learned overfit data and lack smoothness, which can affect predictive performance downstream tasks. To address this limitation, we propose GPCDL, convolutional framewor...

A primary goal of computer experiments is to reconstruct the function given by code via scattered evaluations. Traditional isotropic Gaussian process models suffer from curse dimensionality, when input dimension relatively high limited data points. with additive correlation functions are scalable but they more restrictive as only work for functions. In this work, we consider a projection pursui...

We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point down-weigh the contributions from other points that are far away, and derive GPR model stemming application of such operation. Through set experiments, demonstrate competitive performance proposed approach compared full GPR, localized models, deep proc...