In this paper, we consider a non-parametric regression model relying on Riesz estimators. This linear is similar to the usual since they both rely projection operators. We indicate that estimator relies positive basis elements of finite-dimensional sub-lattice generated by rows some design matrix. A strong motivation for using data explanatory variables may come from categorical variables. Calc...

In this paper, we introduce a new class of bivariate distributions by compounding the bivariate generalized exponential and power-series distributions. This new class contains the bivariate generalized exponential-Poisson, bivariate generalized exponential-logarithmic, bivariate generalized exponential-binomial and bivariate generalized exponential-negative binomial distributions as specia...

Regression models based on the log-symmetric family of distributions are particularly useful when response variable is continuous, positive, and asymmetrically distributed. In this article, we propose derive a class new approach to quantile regression using parameterized by means their quantiles. Two Monte Carlo simulation studies conducted utilizing R software. The first one analyzes performan...

Active learning refers to algorithmic frameworks aimed at selecting training data points in order to reduce the number of required training data points and/or improve the generalization performance of a learning method. In this paper, we present an asymptotic analysis of active learning for generalized linear models. Our analysis holds under the common practical situation of model misspecificat...

In the linear regression model, multicollinearity effects on ordinary least squares (OLS) estimator performance make it inefficient. To solve this, several estimators are given. The Kibria-Lukman (KL) is a recent that has been proposed to problem. this paper, generalized version of KL proposed, along with optimal biasing parameter our derived by minimizing scalar mean squared error. Theoretical...

In an effort to improve the small sample properties of generalized method of moments (GMM) estimators, a number of alternative estimators have been suggested. These include empirical likelihood (EL), continuous updating, and exponential tilting estimators. We show that these estimators share a common structure, being members of a class of generalized empirical likelihood (GEL) estimators. We us...

The maximum spacing (MSP) method, introduced by Cheng and Amin (1983) and independently by Ranneby (1984), is a general method for estimating param­ eters in univariate continuous distributions and is known to give consistent and asymptotically efficient estimates under general conditions. This method can be derived from an approximation based on simple spacings of the Kullback-Leibler informat...

Projection pursuit regression (PPR) can be used to estimate a smooth function of several variables from noisy and scattered data. The estimate is a sum of smoothed one-dimensional projections of the variables. This paper discusses an extension of PPR to exponential family distributions, called generalized projection pursuit regression (GPPR). The proposed model allows multiple responses and non...