The implementation characteristics of two method of L-moments (MLM) algorithms for parameter estimation of the 4-parameter Asymmetric Exponential Power (AEP4) distribution are studied using the R environment for statistical computing. The objective is to validate the algorithms for general...

A  curse of dimensionality  arises when using the Continuum-GMM procedure to estimate large dimensional models. Two solutions are proposed, both of which convert the high dimensional model into a continuum of reduced information sets. Under certain regularity conditions, each reduced...

The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial...

Rate variation among the sites of a molecular sequence is commonly found in applications of phylogenetic inference. Several approaches exist to account for this feature but they do not usually enable the investigator to pinpoint the sites that evolve under one or another rate of evolution in a...

We consider the estimation problem of conditional quantile when multi-dimensional covariates are involved. To overcome the “curse of dimensionality” yet retain model flexibility, we propose two partially linear models for conditional quantiles: partially linear single-index models (QPLSIM)...

An approximation scheme for estimating a fixed, unknown fuzzy set from random samples taken from the nested random set defined by its α-level sets is presented. Its strong consistency is studied, giving rates of convergence in four metrics. A simulation study suggests that the behaviour for...

In the application of the popular maximum likelihood method to factor analysis, the number of factors is commonly determined through a two-stage procedure, in which stage 1 performs parameter estimation for a set of candidate models and then stage 2 chooses the best according to certain model...

The main question is how to compute the upper and lower limits of the range of possible values of a given statistic, when the data range over given intervals. Initially some well-known statistics, such as sample mean, sample variance or F-ratio, are considered in order to illustrate that in some...

Polya tree priors are random probability distributions that are easily centered at standard parametric families, such as the normal. As such, they provide a convenient avenue toward creating a parametric/nonparametric test statistic “blend” for the classic problem of testing whether data...

Several Multivariate GARCH (MGARCH) models have been proposed, and recently such MGARCH specifications have been examined in terms of their out-of-sample forecasting performance. An empirical comparison of alternative MGARCH models is provided, which focuses on the BEKK, DCC, Corrected DCC...