A model for estimating point sources in spatial data

We introduce a point source model which may be useful for estimating point sources in spatial data. It may also be useful for modelling general spatial data, and providing a simple explanatory model for some data, whilst in other cases it may give a parsimonious representation. The model assumes that there are point sources (or sinks), usually at unknown positions, and that the mean value at a site depends on the distance from these sources. We discuss the general form of the model, and some methods for estimating the sources and the regression parameters. We demonstrate the methodology using a simulation study, and apply the model to two real data sets. Some possibilities for further research are outlined.     

Neurocognitive Effects in Welders Exposed to Aluminium: An Application of the NPC Test and NPC Ranking Methods

The aim of this study is to investigate the effects of exposure to aluminium on the cognitive sphere in a group of welders. Given the intrinsic complexity of the neurocognitive sphere, three different psychomotor variables were used for its investigation allowing the resulting problem to be naturally modelized into a multivariate framework and solved by a nonparametric combination of permutation tests (NPC test). In order to make the treated and control groups comparable, we also stratified the samples on the basis of a risk index that estimates the combined action of biological age and exposure time. Despite the fact that studies in the literature have reached conflicting results, our study highlights a significant drop in attention and memory performances in individuals exposed to aluminium. Finally, we identify a global criterion that summarises the information on the neurocognitive state by applying the nonparametric combination of dependent rankings method (NPC ranking).     

INTERTEMPORAL CHOICE AND CONSUMPTION MOBILITY

The theory of intertemporal consumption choice makes sharp predictions about the evolution of the entire distribution of household consumption, not just about its conditional mean. In the paper, we study the empirical transition matrix of consumption using a panel drawn from the Bank of Italy Survey of Household Income and Wealth. We estimate the parameters that minimize the distance between the empirical and the theoretical transition matrix of the consumption distribution. The transition matrix generated by our estimates matches remarkably well the empirical matrix, both in the aggregate and in samples stratified by education. Our estimates strongly reject the consumption insurance model and suggest that households smooth income shocks to a lesser extent than implied by the permanent income hypothesis. (JEL: D52, D91, I30)     

A measure of interrater absolute agreement for ordinal categorical data

Statistical Methods & Applications - A measure of interrater absolute agreement for ordinal scales is proposed capitalizing on the dispersion index for ordinal variables proposed by Giuseppe...     

Gambling Disorder and Affect Regulation: The Role of Alexithymia and Attachment Style

The aim of the present study was to explore the dimensions of alexithymia and attachment styles in a group of disordered gamblers and to evaluate the relationship between alexithymia, attachment styles, and the severity of gambling disorder. Sixty disordered gamblers diagnosed according to the diagnostic and statistical manual-5 filled out the Kurzfragebogen zum Glücksspielverhalten, the 20-Item Toronto Alexithymia Scale, and the Experiences in Close Relationships-Revised. Approximately 70 % of the sample displayed ‘intermediate’ and ‘severe’ gambling severity levels on the Kurzfragebogen zum Glücksspielverhalten, and 77 % showed ‘high’ or ‘borderline’ levels of alexithymia on the Toronto Alexithymia Scale (mean = 56.40). Regarding attachment styles, 70 % of the sample displayed an ‘insecure’ attachment, with a particularly high prevalence of the ‘fearful’ style (26.66 %). A linear regression analysis revealed that only the anxiety dimension of the Experiences in Close Relationships questionnaire predicted the severity of gambling. Our data appear to confirm that gambling disorder is characterised by emotional and relational dysregulation, and that pathological gambling behaviours may serve as external regulators of internal undifferentiated emotional states.     

Revising a Prognostic Index Developed for Classification Purposes: an Application to Gastric Cancer Data

A prognostic index (PI) is usually derived from a regression model as a weighted mean of the covariates, with weights (partial scores) proportional to the parameter estimates. When a PI is applied to patients other than those considered for its development, the issue of assessing its validity on the new case series is crucial. For this purpose, Van Houwelingen (2000) proposed a method of validation by calibration, which limits overfitting by embedding the original model into a new one, so that only a few parameters will have to be estimated. Here we address the problem of PI validation and revision with the above approach when the PI has classification purposes and it represents the linear predictor of a Weibull model, derived from an accelerated failure time parameterization instead of a proportional hazards one, as originally described by Van Houwelingen. We show that the Van Houwelingen method can be applied in a straightforward manner, provided that the parameterization originally used in the PI model is appropriately taken into account. We also show that model validation and revision can be carried out by modifying the cut-off values used for prognostic grouping without affecting the partial scores of the original PI. This procedure can be applied to simplify the clinician's use of an established PI for classification purposes.     

Inverse Sampling with Unequal Selection Probabilities

In some real situations the population of interest is divided into two groups, of which one contains only a few units. In other cases, the population may be considered as subdivided into two group', for example, if only a few units display a value of the variable of interest which is highly different from zero, while all the other units show a value equal to or near zero. In both cases, inverse sampling is more efficient than classical fixed sample-size designs to obtain the parameter estimators for the whole population as well as for its groups (e.g., Salehi and Seber, 2004 Salehi , M. M. , Seber , G. A. F. ( 2004 ). A general inverse sampling scheme and its application to adaptive cluster sampling . Austral. NZ J. Statist. 46 : 483 – 494 .[Crossref], [Web of Science ®] , [Google Scholar]). In fact, in this design the procedure selection continues until a prefixed number of units with the characteristic of interest is sampled. Since it is not known a priori to which group the population units belong, the sample size is a random variable. Christman and Lan (2001 Christman , M. C. , Lan , F. ( 2001 ). Inverse adaptive cluster sampling . Biometrics 57 : 1096 – 1105 .[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) and Salehi and Seber (2001 Salehi , M. M. , Seber , G. A. F. ( 2001 ). A new proof of Murthy's estimator which applies to sequential sampling . Austral. NZ J. Statist. 43 : 281 – 286 . [Google Scholar] 2004 Salehi , M. M. , Seber , G. A. F. ( 2004 ). A general inverse sampling scheme and its application to adaptive cluster sampling . Austral. NZ J. Statist. 46 : 483 – 494 .[Crossref], [Web of Science ®] , [Google Scholar]) considered inverse sampling designs when all the population units have equal selection probabilities. In this article, we consider the general case in which the units may have unequal probabilities of being included in the sample. In fact, in many real situations different units may have different selection probabilities because of some inherent features of the sampling procedure, or in order to obtain better estimates. We derive unbiased estimators of the totals of the two groups, their variance and the corresponding unbiased variance estimators in inverse sampling with replacement. Finally, we derive similar results for more complex designs, where the selection procedure stops before observing the prefixed number of units from the rare group.     

A Permutation Approach to Split-Plot Experiments

Split-plot design may be refer to a common experimental setting where a particular type of restricted randomization has occurred during a planned experiment. The aim of this article is to suggest a new method to perform inference on split-plot experiments by combination-based permutation tests. This novel nonparametric approach has been studied and validated using a Monte Carlo simulation study where we compared it with the parametric and nonparametric procedures proposed in the literature. Results suggest that in each experimental situation where normality is hard to justify and especially when errors have heavy-tailed distribution, the proposed nonparametric procedure can be considered as a valid solution.