This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable serially correlated random errors. The random errors are modeled by an autoregressive time series. We show that the distributions of the feasible semiparametric generalized least squares estimator o...

Introduction of Human Development Index (HDI) by UNDP in early 1990 followed a surge in use of non-parametric and parametric indices for measurement and comparison of countries performance in development, globalization, competition, well-being and etc. The HDI is a composite index of three indicators. Its components are to reflect three major dimensions of human development: longevity, knowledg...

In this paper, we conduct simultaneous inference of the non-parametric part a partially linear model when component is multivariate unknown function. Based on semi-parametric estimates model, construct confidence region function for inference. The developed methodology applied to perform U.S. gasoline demand where income and price variables are contaminated by Berkson errors. empirical results ...

a numerical study has been done through an al2o3–water in a double lid-driven  square  cavity with various  inclination  angles  and discrete heat sources. the top and right moving walls are at low temperature. half of  the  left and bottom walls are  insulated and  the  temperatures of the other half are kept at high. a large number of simulations for a wide  range  of richardson  number  of  ...

Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering of vector inputs is straightforward, extensions to structured data or time-series data remain less explored. This paper proposes a clustering method for time-series data that couples non-parametric spectral clustering with para...

For high-dimensional problems various parametric priors have been proposed to promote sparse solutions. While parametric priors has shown considerable success they are not very robust in adapting to varying degrees of sparsity. In this work we propose a discrete mixture prior which is partially nonparametric. The right structure for the prior and the amount of sparsity is estimated directly fro...

In this paper we discuss models for multiple binary sequences using de Finetti's notions of exchangeability and partial exchangeability as a cornerstone for model building. Thus, we assume each sequence to be a Markov chain conditional on some random transition matrix. This construction is seen as an extension of models based on Polya urns. We consider two particular problems in which the distr...

Identification of protein complexes from proteomic experiments is crucial to understand not only their function but also the principles of cellular organization. Advances in experimental techniques have enabled the construction of large–scale protein–protein interaction networks, and computational methods have been developed to analyze high–throughput data. In most cases several parameters are ...