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The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model order selection step is...
The analysis of the joint cumulative distribution function (CDF) with bivariate event time data is a challenging problem both theoretically and numerically. This paper develops a tensor spline-based s...
The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. How...
Regression spline is a useful tool in nonparametric regression. However, finding the optimal knot locations is a known difficult problem. In this article, we introduce the Non-concave Penalized Regres...
Consider the problem when $X_1,X_2,..., X_n$ are distributed on a circle following an unknown distribution $F$ on $S^1$. In this article we have consider the absolute general set-up where the density ...
Quantile regression predicts the $\tau$-quantile of the conditional distribution of a response variable given the explanatory variable for $\tau\in(0,1)$. The aim of this paper is to establish the asy...
In [5] we have announced a h e a r spllne method for nonparametric density and distribution estimation on the real line. In this paper, asymptotic properties of a large family of such estimators a...
We study the nonparametric spline density estimators of probability density. The equivalence of weak convergence for L,-consistency of one density and completely for L,-consistency of all densities...
We introduce a new class of distributions to model directional data, based on hyperspherical log-splines. The class is very exible and can be used to model data that exhibit features that cannot be ...
Intheconjugate priorforthenormallinear model, theprior variancefor the coecients is a multiple of the error variance parameter. However, if the prior mean for the coecients is poorly chosen, the pos...
A Spline Approach to Nonparametric Test of Hypothesis。
This note proposes a new methodology for function classification with Support Vector Machine (SVM). Rather than relying on projection on a truncated Hilbert basis as in our previous work, we use an im...
For the past two decades, single-index model, a special case of projection pursuit regression, has proven to be an efficient way of coping with the high dimensional problem in nonparametric regressi...

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