A New Regression Model for the Analysis of Overdispersed and Zero-Modified Count Data.

Count datasets are traditionally analyzed using the ordinary Poisson distribution. However, said model has its applicability limited, as it can be somewhat restrictive to handling specific data structures. In this case, the need arises for obtaining alternative models that accommodate, for example, overdispersion and zero modification (inflation/deflation at the frequency of zeros). In practical terms, these are the most prevalent structures ruling the nature of discrete phenomena nowadays. Hence, this paper's primary goal was to jointly address these issues by deriving a fixed-effects regression model based on the hurdle version of the Poisson-Sujatha distribution. In this framework, the zero modification is incorporated by considering that a binary probability model determines which outcomes are zero-valued, and a zero-truncated process is responsible for generating positive observations. Posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the g-prior method. Intensive Monte Carlo simulation studies were performed to assess the Bayesian estimators' empirical properties, and the obtained results have been discussed. The proposed model was considered for analyzing a real dataset, and its competitiveness regarding some well-established fixed-effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian p-value and the randomized quantile residuals were considered for the task of model validation.

A new mixed-effects regression model for the analysis of zero-modified hierarchical count data.

Count data sets are traditionally analyzed using the ordinary Poisson distribution. However, such a model has its applicability limited as it can be somewhat restrictive to handle specific data structures. In this case, it arises the need for obtaining alternative models that accommodate, for example, (a) zero-modification (inflation or deflation at the frequency of zeros), (b) overdispersion, and (c) individual heterogeneity arising from clustering or repeated (correlated) measurements made on the same subject. Cases (a)-(b) and (b)-(c) are often treated together in the statistical literature with several practical applications, but models supporting all at once are less common. Hence, this paper's primary goal was to jointly address these issues by deriving a mixed-effects regression model based on the hurdle version of the Poisson-Lindley distribution. In this framework, the zero-modification is incorporated by assuming that a binary probability model determines which outcomes are zero-valued, and a zero-truncated process is responsible for generating positive observations. Approximate posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the Adaptive Metropolis algorithm. Intensive Monte Carlo simulation studies were performed to assess the empirical properties of the Bayesian estimators. The proposed model was considered for the analysis of a real data set, and its competitiveness regarding some well-established mixed-effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian p -value and the randomized quantile residuals were considered for model diagnostics.

Multiscale seasonal factors drive the size of winter monarch colonies.

Monarch butterflies in eastern North America have declined by 84% on Mexican wintering grounds since the observed peak in 1996. However, coarse-scale population indices from northern US breeding grounds do not show a consistent downward trend. This discrepancy has led to speculation that autumn migration may be a critical limiting period. We address this hypothesis by examining the role of multiscale processes impacting monarchs during autumn, assessed using arrival abundances at all known winter colony sites over a 12-y period (2004-2015). We quantified effects of continental-scale (climate, landscape greenness, and disease) and local-scale (colony habitat quality) drivers of spatiotemporal trends in winter colony sizes. We also included effects of peak summer and migratory population indices. Our results demonstrate that higher summer abundance on northern breeding grounds led to larger winter colonies as did greener autumns, a proxy for increased nectar availability in southern US floral corridors. Colony sizes were also positively correlated with the amount of local dense forest cover and whether they were located within the Monarch Butterfly Biosphere Reserve, but were not influenced by disease rates. Although we demonstrate a demographic link between summer and fine-scale winter population sizes, we also reveal that conditions experienced during, and at the culmination of, autumn migration impact annual dynamics. Monarchs face a growing threat if floral resources and winter habitat availability diminish under climate change. Our study tackles a long-standing gap in the monarch's annual cycle and highlights the importance of evaluating migratory conditions to understand mechanisms governing long-term population trends.

Access and Intensity of Mechanization: Empirical Evidence of Rice Farmers in Southern Ghana

ABSTRACT The medium term development plan of Ghana proposed modernization of agriculture to lead the way in transforming the economy. Providing irrigation infrastructure and enhancing farmer access to farm machinery were major interventions proposed. In line with this, the government has been investing in irrigation infrastructure as well as importing farm machinery under various programmes in recent years. This study analyzed access and intensity of mechanization by rice farmers in southern Ghana. The Shai-Osudoku and Ketu North Districts were purposively selected and a total of 360 farmers were randomly sampled from 16 rice growing communities. In general, the results of the descriptive statistics revealed that about 74 % of farmers were still cultivating rice with considerably low level of mechanization. The double hurdle model was employed to estimate the determinants of access to mechanization and the intensity of mechanization. The empirical results of tier one of the double huddle model revealed that size of land, access to credit, availability of farm machinery, expenditure on labour, agrochemical expenditure, the square of age, and gender positively influenced access to mechanization. Seed expenditure, age and district locations negatively influenced access to mechanization. The empirical results of the tier two of the double hurdle model revealed that distance from farm to nearest mechanization centre, rice income, non-farm income and experience were significant variables that positively influenced intensity of mechanization. Land ownership and household size negatively influenced intensity of mechanization. These results have implications for capacity building and government support for rice farmers in southern Ghana.