In this work, we consider the problem of estimating the parameters and predicting the unobserved or removed ordered data for the progressive type II censored flexible Weibull sample. Frequentist and Bayesian analyses are adopted for conducting the estimation and prediction problems. The likelihood method as well as the Bayesian sampling techniques is applied for the inference problems. The point predictors and credible intervals of unobserved data based on an informative set of data are computed. Markov Chain Monte Carlo samples are performed to compare the so-obtained methods, and one real data set is analyzed for illustrative purposes.

This paper develops a new approach to domain estimation and proposes a new class of ratio estimators that is more efficient than the regression estimator and not depending on any optimality condition using the principle of calibration weightings. Some well-known regression and ratio-type estimators are obtained and shown to be special members of the new class of estimators. Results of analytical study showed that the new class of estimators is superior in both efficiency and biasedness to all related existing estimators under review. The relative performances of the new class of estimators with a corresponding global estimator were evaluated through a simulation study. Analysis and evaluation are presented.

We introduce a four-parameter lifetime distribution called the odd log-logistic generalized Gompertz model to generalize the exponential, generalized exponential and generalized Gompertz distributions, among others. We obtain explicit expressions for the moments, moment-generating function, asymptotic distribution, quantile function, mean deviations and distribution of order statistics. The method of maximum likelihood estimation of parameters is compared by six different methods of estimations with simulation study. The applicability of the new model is illustrated by means of a real data set.

In this paper, we propose a new algorithm to extend support vector machine (SVM) for binary classification to multicategory classification. The proposed method is based on a sequential binary classification algorithm. We first classify a target class by excluding the possibility of labeling as any other classes using a forward step of sequential SVM; we then exclude the already classified classes and repeat the same procedure for the remaining classes in a backward step. The proposed algorithm relies on SVM for each binary classification and utilizes only feasible data in each step; therefore, the method guarantees convergence and entails light computational burden. We prove Fisher consistency of the proposed forward–backward SVM (FB-SVM) and obtain a stochastic bound for the predicted misclassification rate. We conduct extensive simulations and analyze real-world data to demonstrate the superior performance of FB-SVM, for example, FB-SVM achieves a classification accuracy much higher than the current standard for predicting conversion from mild cognitive impairment to Alzheimer’s disease.

In this paper, we make an improvement on the conventional visual hull reconstruction method that runs on a single consumer graphics card. The target application is a high-precision diamond inclusion reconstruction system. One major contribution of this paper is an evaluation system of voxels for high-precision reconstruction. In contrast to existing approaches, it allows us to reconstruct the thin structure of the diamond inclusion. Based on this, we obtain a significant improvement in reconstruction precision, especially for thin inclusion.

In this paper, we introduce a new averaging rule, the nonlinear weighted averaging rule. As an application, this averaging rule is used to replace the midpoint averaging in the de Casteljau evaluation algorithm and with this scheme we can also generate transcendental functions which cannot be generated by the classical de Casteljau algorithm. We also investigate the properties of the curves of the functions generated by blossoming, where the results show that these curves and the classical Bézier curves have some similar properties, including variation diminishing property and endpoint interpolation. However, the curves obtained by blossoming using nonlinear weighted averaging rules induced by certain functions violate some properties like convex hull property.