Simultaneous tolerance intervals in the random one-way model with covariates

Simultaneous tolerance intervals developed by Limam and Thomas (19881, for the normal regression model, are generalized to the random one-way model with covariates. Simultaneous tolerance intervals for unit means are developed for the balanced model. A simulation study is used to estimate the exact confidence of the tolerance intervals for models with one covariate.     

On a theorem of graybill

Graybill (1976) gives a theorem for testing variance components in balanced random models. Unfortunately, the theorem does not hold true. We pin down the reason that causes the theorem to fail and give a correct version of it.     

Poverty Reduction and Growth Interactions: What Can Be Learned from the Syrian Experience?

The aim of this article is to analyse in depth the interactions of growth and poverty in Syria, which undertook reforms to reduce the government's involvement in the economy. During the 1996–2004 period, growth was pro‐poor in ‘weak absolute’ terms but not in either relative or ‘strong absolute’ terms, owing to the increase in inequality. This can be explained partly by tax policies, but also by an unequal distribution of investment at the regional level. There was also a widening of the gap between urban and rural areas, mainly owing to a pattern of growth in which oil played an increasing role and agriculture a decreasing one. Agricultural and land‐policy reforms could have had a negative impact on poverty, despite their positive effect on productivity.     

Comparison of non-parametric and semi-parametric tests in detecting long memory

The first two stages in modelling times series are hypothesis testing and estimation. For long memory time series, the second stage was studied in the paper published in [M. Boutahar et al., Estimation methods of the long memory parameter: monte Carlo analysis and application, J. Appl. Statist. 34(3), pp. 261–301.] in which we have presented some estimation methods of the long memory parameter. The present paper is intended for the first stage, and hence completes the former, by exploring some tests for detecting long memory in time series. We consider two kinds of tests: the non-parametric class and the semi-parametric one. We precise the limiting distribution of the non-parametric tests under the null of short memory and we show that they are consistent against the alternative of long memory. We perform also some Monte Carlo simulations to analyse the size distortion and the power of all proposed tests. We conclude that for large sample size, the two classes are equivalent but for small sample size the non-parametric class is better than the semi-parametric one.     

Generalized Steiner Problems and Other Variants

In this paper, we examine combinatorial optimization problems by considering the case where the set N (the ground set of elements) is expressed as a union of a finite number of m nonempty distinct subsets N ₁,...,N _m. The term we use is the generalized Steiner problems coined after the Generalized Traveling Salesman Problem. We have collected a short list of classical combinatorial optimization problems and we have recast each of these problems in this broader framework in an attempt to identify a linkage between these “generalized” problems. In the literature one finds generalized problems such as the Generalized Minimum Spanning Tree (GMST), Generalized Traveling Salesman Problem (GTSP) and Subset Bin-packing (SBP). Casting these problems into the new problem setting has important implications in terms of the time effort required to compute an optimal solution or a “good” solution to a problem. We examine questions like “is the GTSP “harder” than the TSP?” for a number of paradigmatic problems starting with “easy” problems such as the Minimal Spanning Tree, Assignment Problem, Chinese Postman, Two-machine Flow Shop, and followed by “hard” problems such as the Bin-packing, and the TSP.     

Estimation Methods of the Long Memory Parameter: Monte Carlo Analysis and Application

Since the seminal paper of Granger & Joyeux (1980), the concept of a long memory has focused the attention of many statisticians and econometricians trying to model and measure the persistence of stationary processes. Many methods for estimating d, the long-range dependence parameter, have been suggested since the work of Hurst (1951). They can be summarized in three classes: the heuristic methods, the semi-parametric methods and the maximum likelihood methods. In this paper, we try by simulation, to verify the two main properties of [dcirc]: the consistency and the asymptotic normality. Hence, it is very important for practitioners to compare the performance of the various classes of estimators. The results indicate that only the semi-parametric and the maximum likelihood methods can give good estimators. They also suggest that the AR component of the ARFIMA (1, d, 0) process has an important impact on the properties of the different estimators and that the Whittle method is the best one, since it has the small mean squared error. We finally carry out an empirical application using the monthly seasonally adjusted US Inflation series, in order to illustrate the usefulness of the different estimation methods in the context of using real data.     

Dual divergences estimation for censored survival data

This paper is devoted to robust estimation based on dual divergences estimators for parametric models in the framework of right censored data. We give limit laws of the proposed estimators and examine their asymptotic properties through a simulation study.     

External Assets as Predictors of Positive Emotions Among At‐Risk Youth in Malaysia

The present study is among the first to examine external assets as predictors of positive emotions among at‐risk youth. The study aims to examine the associations of external assets with positive emotions, determine external assets as a predictor of positive emotions in phase 1, and see if these predictors were consistently established in phase 2. At first contact, 403 participants from low‐income apartments in the suburbs of Kuala Lumpur, aged 13–25 years were asked to complete the 25 Developmental Assets, Malaysian version. The participants were also invited to participate in social activities organized by the PERMATA community. The same participants were approached four months later to examine the stability of measures. Multiple regression analysis revealed support is the most significant predictor of positive emotions at phase 1 whilst positive peer influence, family boundaries and caring neighborhood are the significant predictors at phase 2. Results suggested that the presence of other external assets can enhance the positive development of at‐risk youth, however, the support must be present to some extent in the first place.     

Residual Responses to Change Patterns of Autocorrelated Processes

This article studies the residual behaviour of various stationary processes in the presence of change patterns. Three types of change patterns are considered, Additive Outliers, Innovative Outliers and Level Shift. The knowledge of the residual behaviour is important for monitoring production processes. A new method of residual process control is proposed, the patterns chart. In addition to the advantage of detecting change patterns, it distinguishes their nature. The patterns chart's performance is compared to the performance of the special causes control (SCC) chart based on average run length. The results show that the proposed method performs better than a SCC chart. A real case study illustrates that the patterns chart has all the desirable properties of a SCC chart and it overcomes the negative ones.