This work proposes a quantitative, non-parametric interpretation of statistical performance of stochastic multiobjective optimizers, including, but not limited to, genetic algorithms. It is shown that, according to this interpretation, typical performance can be defined in terms analogous to the notion of median for ordinal data, as can other measures analogous to other quantiles. Non-parametri...

We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and maintaining a different model for each set within a cover. Inference remains tractable by specifying the probabilistic model in terms of a ran...

In randomized clinical trials, nonparametric approaches are considered when assumptions of a parametric approach are not reasonable. Nonparametric approaches have typically concentrated on hypothesis testing and, unlike parametric approaches, have not been amenable to providing measures of treatment efficacy. If X and Y denote the random variables representing the responses on two treatments A ...

Abstract: The real decentralized fixed mode radius measures how “near” a decentralized LTI system is from having a decentralized fixed mode (DFM) present. In this paper, some properties of the real DFM radius are discussed, a procedure for computing the actual system parametric perturbations that achieve the real DFM radius is presented, and the real DFM radius is extended to deal with structur...

Garment virtual design has been evolved significantly with the rapid development of 3D CAD tools, especially with the convenient availability of NURBS surface modeling capability. Parametric development of clothes is demanded in line with the trend of mass customization according to the true measures of customers or regulated sizes of certain markets. Virtual design features with welldefined as...

We explore the in- and out-of-sample robustness of tests for choice inconsistencies based on parameter restrictions in parametric models, focusing on tests proposed by Ketcham, Kuminoff, and Powers (2016). We argue that their nonparametric alternatives are inherently conservative with respect to detecting mistakes. We then show that our parametric model is robust to KKP's suggested specificatio...

This paper explores the use of a non-parametric frontier approach to analyse multi-factor productivity across time and countries. We argue that conventional measures of total factor productivity involve some restrictive assumptions that might bias the results. A non-parametric approach avoids these assumptions. The model uses linear programming techniques to examine the productivity catching-up...

In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of an existing probabilistic generative model: Parametric Mixture Model(PMM) by hierarchical Bayes model. PMM models multiple-topic documents by mixing model parameters of each single topic with an equal mixture ratio. P...

We review and extend the now considerable literature on Lévy copulas. First, we focus on Monte Carlo methods and present a new robust algorithm for the simulation of multidimensional Lévy processes with dependence given by a Lévy copula. Next, we review statistical estimation techniques in a parametric and a non-parametric setting. Finally, we discuss the interplay between Lévy copulas and mult...

We compare the ability of the parametric Black and Scholes, Corrado and Su models, and Artificial Neural Networks to price European call options on the S&P 500 using daily data for the period January 1998 to August 2001. We use several historical and implied parameter measures. Beyond the standard neural networks, in our analysis we include hybrid networks that incorporate information from the ...