Testing for abrupt breaks in variance structures with smooth changes

Abstract

We investigate the problem of testing for variance breaks in the case where the variance structure is assumed to be smoothly time-varying under the null. Since the classical tests are aimed to detect any change in the variance, they are not able to distinguish between smooth non constant variance and abrupt breaks. In this paper a new procedure for detecting variance breaks taking into account for smooth changes in the variance under the null is proposed. The finite sample properties of the test we introduce are investigated by Monte Carlo experiments. The theoretical outputs are illustrated using U.S. macroeconomic data.     

Investigation of the diesel consumption for trucks at an overburden stripping area by SPC study

This study examined whether diesel consumption used by trucks at a stripping area is controlled or not. The factors affecting diesel consumption were also investigated and some necessary solutions were presented. Diesel consumption was observed with the aid of control graphs. Abnormal situations in the diesel consumption were explored by means of Shewhart control graphs. The factors which are out of control were also presented in a cause–effect diagram, and suggestions for improvement were proposed. It has been determined that the main effect of the diesel consumption is the daily run number of the trucks. The main factors affecting the daily run number were also investigated.     

Suicidal behavior and associated factors in sexually abused adolescents

Objectives

The aim of the present study is to determine the rate of suicidal ideation and suicide attempts in sexually abused adolescents and to determine the factors associated with suicidal behavior.

Method

106 adolescents between the ages of 12–18 (94 girls, 12 boys) who were exposed to sexual abuse and referred for the preparation of forensic reports were included in the study. The investigators retrospectively examined the case files, forensic reports and social worker reports.

Results

It has been found that in cases exposed to sexual abuse, suicidal ideation developed in 63.2% (n = 67) of the individuals and 24.5% (n = 26) of them attempted suicide. The rate of suicidal ideation and suicide attempts was higher in cases who live separately from one or both parents, those who have ASD or PTSD after sexual abuse, cases in which abuse involved penetration, cases who were abused by someone known or someone from the family, cases where sexual abuse was accompanied by coercion and physical violence, and those who were exposed to abuse continuously. Among girls, in cases who have suicide history before sexual abuse and those who were abused by more than one abuser, the rate of suicidal ideation was higher.

Conclusion

It has been shown in many studies that sexual abuse in childhood increases the risk of suicidal behavior. Consideration of the factors associated with suicidal behavior in victims of sexual abuse will be helpful to clinicians in preventing suicide attempts.     

Computing the Exact Fisher Information Matrix of Periodic State-Space Models

The purpose of this article is to develop algorithms for computing the exact Fisher information matrix of periodic time-varying state-space models. We first present a relatively simple recursive algorithm which computes the elements of the exact information matrix without involving numerical differentiation, since all required derivatives are analytically evaluated. The proposed algorithm extends the procedure due to Cavanaugh and Shumway (1996 Cavanaugh , J. E. , Shumway , R. H. ( 1996 ). On computing the expected Fisher information matrix for state-space model parameters . Statist. Probab. Lett. 26 : 347 – 355 .[Crossref], [Web of Science ®] , [Google Scholar]) to the periodic state-space framework. Exploiting the approach used in Klein et al. (2000 Klein , A. , Mélard , G. , Zahaf , T. ( 2000 ). Construction of the exact Fisher information matrix of Gaussian time series models by means of matrix differential rules . Linear Alg. Applic. 321 : 209 – 232 .[Crossref], [Web of Science ®] , [Google Scholar]), a second algorithm is proposed in order to obtain the exact information matrix as a whole instead of element by element. The algorithms are first developed in a general framework and then specialized to the case of a periodic Gaussian vector autoregressive moving-average (PVARMA) model.     

Adaptive estimation of vector autoregressive models with time-varying variance: Application to testing linear causality in mean

Linear vector autoregressive (VAR) models where the innovations could be unconditionally heteroscedastic are considered. The volatility structure is deterministic and quite general, including breaks or trending variances as special cases. In this framework we propose ordinary least squares (OLS), generalized least squares (GLS) and adaptive least squares (ALS) procedures. The GLS estimator requires the knowledge of the time-varying variance structure while in the ALS approach the unknown variance is estimated by kernel smoothing with the outer product of the OLS residual vectors. Different bandwidths for the different cells of the time-varying variance matrix are also allowed. We derive the asymptotic distribution of the proposed estimators for the VAR model coefficients and compare their properties. In particular we show that the ALS estimator is asymptotically equivalent to the infeasible GLS estimator. This asymptotic equivalence is obtained uniformly with respect to the bandwidth(s) in a given range and hence justifies data-driven bandwidth rules. Using these results we build Wald tests for the linear Granger causality in mean which are adapted to VAR processes driven by errors with a nonstationary volatility. It is also shown that the commonly used standard Wald test for the linear Granger causality in mean is potentially unreliable in our framework (incorrect level and lower asymptotic power). Monte Carlo experiments illustrate the use of the different estimation approaches for the analysis of VAR models with time-varying variance innovations.     

Particle swarm optimization based-algorithms to solve the two-machine cross-docking flow shop problem: just in time scheduling

Cross-docking is an innovative logistical strategy which provides less inventory holding costs, less transportation costs and fast customer deliveries without storage in between or less than 24 hours. In this paper, we address the two-machine cross-docking scheduling problem within a Just-In-Time (JIT) context. This latter requires the punctuality and exactness of product deliveries. To satisfy this target, we aim to minimize the total earliness and tardiness, then early or tardy deliveries are discouraged. This study presents a great contribution in solving such NP-hard problem while applying different versions of the PSO (Particle Swarm Optimization) algorithm. One of them is hybridized with the Genetic Algorithm (GA). This latter is then shown to be the best one over computational experiments using different sized instances and by determining a percentage deviation from a developed lower bound.

     

A Note on Calculating Autocovariances of Periodic ARMA Models

An analytically simple and tractable formula for the start-up autocovariances of periodic ARMA (PARMA) models is provided.