The Kumaraswamy Lindley Regression Model with Application on the Egyptian Stock Exchange

We introduce and study the Kumaraswamy Lindely Distribution (KLD) model, which has increasing, decreasing, upside-down bathtub and bathtub shaped hazard functions.. We perform a Monte Carlo simulation study to assess the finite sample behavior of the maximum likelihood estimates of the parameters. We define a new regression model based on the new distribution. The new regression was applied to data from the Egyptian stock exchange in the period of (2015-2019). Finally, we study some properties of regression Residual analysis The martingale residual, Deviance component residual.

Spatial and spatio-temporal analysis of Salmonella infection in dairy herds in England and Wales

SUMMARYUsing data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0·001) and borderline evidence was found for spatial clustering of S. Typhimurium (P=0·077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.

Effect of photoscale, interpreter bias and land type on woody crown-cover estimates from aerial photography

Woody vegetation cover interpreted from aerial photography requires assessment against field data as the signature of woody vegetation cover may differ between photoscales, vegetation types and photo-interpreters. Measurements of aerial woody cover taken from aerial photography of four different photoscales were compared with a field dataset from Eucalyptus- and Acacia-dominated landscapes of semi-arid Queensland. Two interpreters employed a method that utilises a stereoscope and sample-point graticule for manual quantified measurements of aerial woody cover. Both interpreters generated highly significant models accounting for 77 and 78% of deviance. Photoscale appears to have a consistent effect whereby the signature of woody cover increases as the photoscale decreases from 1 : 25 000 to 1 : 80 000, although the magnitude of this effect was different between interpreters. The results suggest no substantial differences in the shape of models predicting crown cover between Acacia- and Eucalyptus-dominated land types, although the precision of the models was greater for the Acacia (90–91% of residual deviance) than for the Eucalyptus (50–56% of residual deviance) land type. The reduced accuracy in the Eucalyptus land type probably reflects the relatively diffuse crowns of the dominant trees. The models generated for this dataset are within the range of those from other calibration studies employing photography of a range of scales and methodologies. The effect of photoscale is verified between the available studies, but there may also be variations arising from methodological differences or image properties. The present study highlights the influence of photoscale and interpreter bias for assessing woody crown cover from aerial photography. Studies that employ aerial photography should carefully consider potential biases and cater for them by calibrating assessments with field measurements.

The beauty of models for developmental disorders

Cognitive frameworks provide important means for uniting concepts of specificity, cognition, and dynamic change in development. Two points are challenged by evidence from special populations: (1) that boundary constraints such as Residual Normality and a cognitive “endstate” compromise the use of cognitive models; and (2) the developmental process itself automatically rejects either Residual Normality or residual deviance from typical development.