Two new confidence intervals for the coefficient of variation in a normal distribution

In this article we introduce an approximately unbiased estimator for the population coefficient of variation, τ, in a normal distribution. The accuracy of this estimator is examined by several criteria. Using this estimator and its variance, two approximate confidence intervals for τ are introduced. The performance of the new confidence intervals is compared to those obtained by current methods.     

Combining Bayesian method and Kalman smoother for detection additive outlier patches in autoregressive time series

ABSTRACT

This article proposes a development of detecting patches of additive outliers in autoregressive time series models. The procedure improves the existing detection methods via Gibbs sampling. We combine the Bayesian method and the Kalman smoother to present some candidate models of outlier patches and the best model with the minimum Bayesian information criterion (BIC) is selected among them. We propose that this combined Bayesian and Kalman method (CBK) can reduce the masking and swamping effects about detecting patches of additive outliers. The correctness of the method is illustrated by simulated data and then by analyzing a real set of observations.     

Economic design of &Xmacron; charts with variable parameters: the Markov chain approach

This paper presents an economic design of &Xmacron; control charts with variable sample sizes, variable sampling intervals, and variable control limits. The sample size n, the sampling interval h, and the control limit coefficient k vary between minimum and maximum values, tightening or relaxing the control. The control is relaxed when an &Xmacron; value falls close to the target and is tightened when an &Xmacron; value falls far from the target. A cost model is constructed that involves the cost of false alarms, the cost of finding and eliminating the assignable cause, the cost associated with production in an out-of-control state, and the cost of sampling and testing. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A comprehensive study is performed to examine the economic advantages of varying the &Xmacron; chart parameters.     

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.     

Randomized singular spectrum analysis for long time series

ABSTRACT

Singular spectrum analysis (SSA) is a relatively new method for time series analysis and comes as a non-parametric alternative to the classical methods. This methodology has proven to be effective in analysing non-stationary and complex time series since it is a non-parametric method and do not require the classical assumptions over the stationarity or over the normality of the residuals. Although SSA have proved to provide advantages over traditional methods, the challenges that arise when long time series are considered, make the standard SSA very demanding computationally and often not suitable. In this paper we propose the randomized SSA which is an alternative to SSA for long time series without losing the quality of the analysis. The SSA and the randomized SSA are compared in terms of quality of the model fit and forecasting, and computational time. This is done by using Monte Carlo simulations and real data about the daily prices of five of the major world commodities.