Search results for "Linear Model"

Count data in psychological applied research.

2006

As some authors have noticed in fields other than psychology, level of measurement and distributional characteristics of count data are commonly not taken into account, so that they are analysed as normally distributed continuous variables, and therefore some general linear model is applied. In this work, we review a random sample of 457 articles published in the last four years in journals with the highest impact factor in the Journal Citation Reports (JCR Social Sciences Edition) of the Institute for Scientific Information. The goals are to know how often count variables appear in psychological applied research and which data analyses are used when dealing with count response variables. …

Irregular motion recovery in fluorescein angiograms

1997

Abstract Fluorescein angiography is a common procedure in ophthalmic practice, mainly to evaluate vascular retinopathies and choroidopathies from sequences of ocular fundus images. In order to compare the images, a reliable overlying is essential. This paper proposes some methods for the recovery of irregular motion in fluorescein angiograms (FA). The overlying is done by a three step procedure: detection of relevant points, matching points from different images and estimation of the assumed linear geometric transformation. A stochastic model (closely related to the general linear model) allows to fuse the second and third steps. Two different estimators of the geometric transformation are …

Multivariate versus univariate calibration for nonlinear chemiluminescence data

2001

Abstract Multivariate calibration is tested as an alternative to model chromium(III) concentration versus chemiluminescence registers obtained from luminol-hydrogen peroxide reaction. The multivariate calibration approaches included have been: conventional linear methods (principal component regression (PCR) and partial least squares (PLS)), nonlinear methods (nonlinear variants and variants of locally weighted regression) and linear methods combined with variable selection performed in the original or in the transformed data (stepwise multiple linear regression procedure). Both the direct and inverse univariate approaches have been also tested. The use of a double logarithmic transformatio…

The Norm-P Estimation of Location, Scale and Simple Linear Regression Parameters

1989

A new formulation of the exponential power distributions is used as general error model to describe long-tailed and short -tailed distributed errors. The proposed estimators of the location, scale and structure parameters of this general model and of the simple linear regression parameters when the response variable is affected by errors coming from the previous model should be used instead of robust estimators and against the practice of rejecting outlying observations. Two Monte Carlo simulations prove the good properties of these norm-p estimators.

Habitat preferences of edible dormouse, Glis glis italicus: implications for the management of arboreal mammals in Mediterranean forests

2015

Research on arboreal mammals living in Mediterranean forests is poor. Molecular research assessed the existence of an evolutionary significant unit in the edible dormouse populations living in south Italy, Sicily and Sardinia, and we decided to investigate the environmental factors capable of explaining its occurrence and abundance in Sicily, for a better management of these populations. We assessed the species habitat preferences by setting 25 large and 25 small nestboxes in five sample areas along an altitudinal gradient of the Madonie Range, and recorded habitat variables, food availability, and demographic data for two years. To obtain synthetic descriptors of the dormice habitat requir…

Model averaging estimation of generalized linear models with imputed covariates

2015

a b s t r a c t We address the problem of estimating generalized linear models when some covariate values are missing but imputations are available to fill-in the missing values. This situation generates a bias-precision trade- off in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a problem of model uncertainty using Bayesian averaging of classical maximum likelihood estimators (BAML). We also propose a block model averaging strategy that incorporates information on the missing-data patterns and is computationally simple. An empirical application illustrates our…

Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation

2018

The paper presents a microsimulation-based approach for roundabout safety performance evaluation. Based on a sample of Slovenian roundabouts, the vehicle trajectories exported from AIMSUN and VISSIM were used to estimate traffic conflicts using the Surrogate Safety Assessment Model (SSAM). AIMSUN and VISSIM were calibrated for single-lane, double-lane and turbo roundabouts using the corresponding empirical capacity function which included critical and follow-up headways estimated through meta-analysis. Based on calibration of the microsimulation models, a crash prediction model from simulated peak hour conflicts for a sample of Slovenian roundabouts was developed. A generalized linear model…

Weighted-average least squares estimation of generalized linear models

2018

The weighted-average least squares (WALS) approach, introduced by Magnus et al. (2010) in the context of Gaussian linear models, has been shown to enjoy important advantages over other strictly Bayesian and strictly frequentist model averaging estimators when accounting for problems of uncertainty in the choice of the regressors. In this paper we extend the WALS approach to deal with uncertainty about the specification of the linear predictor in the wider class of generalized linear models (GLMs). We study the large-sample properties of the WALS estimator for GLMs under a local misspecification framework that allows the development of asymptotic model averaging theory. We also investigate t…

Using the dglars Package to Estimate a Sparse Generalized Linear Model

2015

dglars is a publicly available R package that implements the method proposed in Augugliaro et al. (J. R. Statist. Soc. B 75(3), 471-498, 2013) developed to study the sparse structure of a generalized linear model (GLM). This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve. dglars is a publicly available R package that implements the method proposed in Augugliaro et al. (J. R. Statist. Soc. B 75(3), 471-498, 2013) developed to study the sparse structure of a generalized linear model (GLM). This method, call…

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…