Search results for "Generalized linear model"

Modeling Posidonia oceanica growth data: from linear to generalized linear mixed models

2010

The statistical analysis of annual growth of Posidonia oceanica is traditionally carried out through Gaussian linear models applied to untransformed, or log-transformed, data. In this paper, we claim that there are good reasons for re-considering this established practice, since real data on annual growth often violate the assumptions of Gaussian linear models, and show that the class of Generalized Linear Models (GLMs) represents a useful alternative for handling such violations. By analyzing Sicily PosiData-1, a real dataset on P. oceanica growth data gathered in the period 2000–2002 along the coasts of Sicily, we find that in the majority of cases Normality is rejected and the effect of …

Differential geometric least angle regression: a differential geometric approach to sparse generalized linear models

2013

Summary Sparsity is an essential feature of many contemporary data problems. Remote sensing, various forms of automated screening and other high throughput measurement devices collect a large amount of information, typically about few independent statistical subjects or units. In certain cases it is reasonable to assume that the underlying process generating the data is itself sparse, in the sense that only a few of the measured variables are involved in the process. We propose an explicit method of monotonically decreasing sparsity for outcomes that can be modelled by an exponential family. In our approach we generalize the equiangular condition in a generalized linear model. Although the …

A differential-geometric approach to generalized linear models with grouped predictors

2016

We propose an extension of the differential-geometric least angle regression method to perform sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a group equiangularity condition, which is shown to be related to score statistics. An adaptive version, which includes weights based on the Kullback-Leibler divergence, improves its variable selection fea…

Spatial analysis of lanner falcon habitat preferences: Implications for agro-ecosystems management at landscape scale and raptor conservation

2014

Abstract Sicily hosts the largest European population of the endangered lanner falcon, a poorly known species which needs conservation planning based on habitat preferences. A distribution model on 10 × 10 km cells of Sicily was described using Generalized Linear Models and variation partitioning methods. This modelling approach extracted explanatory factors, pure and joint effects of greatest influence from subsets of variables controlled for multi-collinearity and spatial autocorrelation. Analytical cartography used the environmental favourability function to assess habitat preferences, and the insecurity index estimated the degree to which lanner falcon occupancy is represented in the Na…

speedglm: Fitting Linear and Generalized Linear Models to large data sets.

2009

This is an R packge to fit (generalized) linear models to large data sets. For data loaded in R memory the fitting is usually fast, especially if R is linked against an optimized BLAS. For data sets of size greater of R memory, the fitting is made by an updating algorithm

A Study on service quality performance of Sicilian hospitals

2007

Estimation of sparse generalized linear models: the dglars package

2013

dglars is a public available R package that implements the method proposed in Augugliaro, Mineo and Wit (2013) developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method (LARS). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve; specifically a predictor-corrector algorithm and a cyclic coordinate descent algorithm.

Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe

2019

Las últimas décadas han visto un interés cada vez mayor en la IED, y un debate creciente sobre su modelización en términos de las variables consideradas como sus determinantes, la especificación del modelo y los métodos de estimación del modelo de gravedad de la IED. Esto se debe a la incertidumbre que rodea tanto las teorías como los enfoques empíricos de la IED. Esta Tesis doctoral tiene como objetivo contribuir a la literatura mediante la investigación de las fuerzas impulsoras de las actividades de las EMNs hacia y desde los países europeos, tanto a nivel regional como nacional, abordando los problemas de selección de variables e incertidumbre del modelo que se enfrentan al modelizar la…

Refugees’ perception of their new life in Germany

2021

Since 2015, Germany has been hosting noticeable incoming flows of refugees and asylum seekers and despite the quality of life of refugees is expected to be improved in the aftermath of their arrival to Germany, refugees are still facing several problems of integration and economic deprivation. Using a sample of individuals from the first wave of the German IAB-BAMF-SOEP Survey of Refugees, we present some preliminary analyses on their life satisfaction (LS). A gamma glm was estimated to focus on the association among levels of LS and main socio-demographic characteristics as well as post-migration factors. Greater stability (both in the legal and personal sphere) in refugees’ lives is posit…

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility U…

2020

Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that…