In this article we use Monte Carlo analysis to assess the small sample behaviour of the OLS, the weighted least squares (WLS) and the mixed effects metaestimators under several types of effect size heterogeneity, using the bias, the mean squared error and the size and power of the statistical tests as performance indicators. Specifically, we analyse the consequences of heterogeneity in effect s...

A two-stage symmetric regression quantile is considered as an alternative for estimating the population quantile for the simultaneous equations model. We introduce a two-stage symmetric trimmed least squares estimator (LSE) based on this quantile. It is shown that, under mixed multivariate normal errors, this trimmed LSE has asymptotic variance much closer to the Cramér-Rao lower bound than som...

The aim of the paper is to present a study on the high-order latent variables for the partial least squares path modelling (PLS-PM). A Monte Carlo simulation study is proposed for comparing the performances of the two bestknown methods for modelling higher-order constructs, namely the repeated indicators and the twostep approaches. The simulation results, far from covering all the potential use...

We study statistical inference in quantile autoregression models when the largest autoregressive coefficient may be unity. The limiting distribution of a quantile autoregression estimator and its t-statistic is derived. The asymptotic distribution is not the conventional Dickey-Fuller distribution, but a linear combination of the Dickey-Fuller distribution and the standard normal, with the weig...

In the 20 years following the publication of the ARCH model, there has been a vast quantity of research uncovering the properties of competing volatility models. Wide-ranging applications to financial data have discovered important stylized facts and illustrated both the strengths and weaknesses of the models. There are now many surveys of this literature. This paper looks forward to identify p...

This paper reports the first application of a new technique to measure the β-decay half lives of exotic nuclei in complex background conditions. Since standard tools were not adapted to extract the relevant information, a new analysis method was developed. The time distribution of background events is established by recording time correlations in backward time. The β half lives of the nuclides ...

In this paper, the problem of identifying stochastic linear discrete-time systems from noisy input/output data is addressed. The input noise is supposed to be white, while the output noise is assumed to be coloured. Some methods based on instrumental variable techniques are studied and compared to a least squares bias compensation scheme with the help of Monte Carlo simulations.

Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model a...

Random coefficient regression models have received considerable attention, especially from econometricians. Previous work has assumed that the coefficients have normal distributions. The variances of the coefficients have, in previous papers, been estimated by maximum likelihood or by least squares methodology applied to the squared residuals from a preliminary (unweighted) fit. Maximum likelih...

In this paper, the problem of identifying stochastic linear discrete-time systems from noisy input/output data is addressed. The input noise is supposed to be white, while the output noise is assumed to be coloured. Some methods based on instrumental variable techniques are studied and compared to a least squares bias compensation scheme with the help of Monte Carlo simulations.