Decision-making in survey sampling planning is a tricky situation; sometimes it involves multiple objectives, with various decision variables emanating from heterogeneous and homogeneous populations. Dealing the entire population under study its uncertain nature becomes challenging issue for researchers policymakers. Hence, an appropriate design optimization methodology are imperative. The pres...

Wepresent a new variationalmethod formesh segmentation by fitting quadric surfaces. Each component of the resulting segmentation is represented by a general quadric surface (including plane as a special case). A novel energy function is defined to evaluate the quality of the segmentation, which combines both L2 and L2,1 metrics from a triangle to a quadric surface. The Lloyd iteration is used t...

As is typical of stochastic-optimization problems, the multivariate integration of the probability-density function is the most difficult task in the optimal allotment of tolerances. In this paper, a truncated Monte Carlo simulation and a genetic algorithm are used as analysis (i.e. multivariate-integration) and synthesis (i.e. optimization) tools, respectively. The new method has performed rob...

We propose a multivariate quantile regression framework that exploits the factor structure in multivariate conditional quantiles through nuclear norm regularization. Because the incurred optimization problem can only be solved approximately, we develop a non-asymptotic upper bound for the estimation error that takes into account the optimization error. We specify an algorithm to compute an appr...

In simulation-based optimization of queuing systems, the presence of discrete-valued parameters (such as buffer sizes and the number of servers) makes the optimization difficult. We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods. Unlike spatial interpolation, our embedding...

assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. sspco optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. one of the things that smart algorithms are applied to solve is the problem ...

Bootstrapping is a computer-intensive statistical method which treats the data set as a population and draws samples from it with replacement. This resampling method has wide application areas especially in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a spe...