Linear programming is one of the most important usages of operation research methods in real life, that includes of one objective function and one or several constraints which can be in the form of equality and inequality. Most of the problems in the real world are include of inconsistent and astute uncertainty, because of this reason we can’t obtain the optimal solution easily. In this paper, ...

Estimating the aggregated market demand for a product in a dynamic market is critical to manufacturers and retailers. Motivated by the need for a statistical demand prediction model for laptop pricing at Hewlett-Packard, we have developed a novel boosting-based varying-coefficient regression model. The developed model uses regression trees as the base learner, and is generally applicable to var...

we present a model and propose an approach to compute an approximate solution of fully fuzzy linear system $(ffls)$ of equations in which all the components of the coefficient matrix are either nonnegative or nonpositive. first, in discussing an $ffls$ with a nonnegative coefficient matrix, we consider an equivalent $ffls$ by using an appropriate permutation to simplify fuzzy multiplications. t...

The charge transport equation is affected by thermal and concentration gradients as well as any applied electric field. In the charge transport equation the electrical conductivity, the electrical diffusivity and the electrical thermophoresis coefficient are all dependent on the electrical mobility which in turn is dependent on the collision time. When a time varying electric field is applied t...

Varying coefficient models have been widely used in longitudinal data analysis, nonlinear time series, survival analysis, and so on. They are natural non-parametric extensions of the classical linear models in many contexts, keeping good interpretability and allowing us to explore the dynamic nature of the model. Recently, penalized estimators have been used for fitting varying-coefficient mode...

Classic methods have been well reported to measure the correlation coefficient of crisp observed data. However, the uncertainty in the real world sometimes makes crisp data unavailable, especially for linguistic variables. Under this situation, fuzzy numbers are often involved in the observed data, but classic statistical methods cannot be directly used to calculate the correlation coefficient ...

This paper is concerned with feature screening and variable selection for varying coefficient models with ultrahigh dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency...

The theory of the real-coefficient linear-phase filterbank (LPFB) is extended to the complex case in two ways, leading to two generalized classes of -channel filterbanks. One is the symmetric/antisymmetric filterbank (SAFB), where all filters are symmetric or antisymmetric. The other is the complex linear phase filterbank (CLPFB), where all filters are Hermitian symmetric or Hermitian antisymme...

Many real-world networks display a natural bipartite structure. It is necessary and important to study the bipartite networks by using the bipartite structure of the data. Here we propose a modification of the clustering coefficient given by the fraction of cycles with size four in bipartite networks. Then we compare the two definitions in a special graph, and the results show that the modifica...