In our daily lives we assist to an exponential growth of mobile and fixed devices that surround us, though many of them having limited resources, and not even providing an interface screen. In this paper, we present remoteU¡, a middleware that allows the interaction of those devices with users, resorting to simple but expressive programming mechanisms, and providing efficient implementation and...

Background: Two main issues that challenge model building are number of Events Per Variable and multicollinearity among exploratory variables. Our aim is to review statistical methods that tackle these issues with emphasize on penalized Lasso regression model.  The present study aimed to explain problems of traditional regressions due to small sample size and m...

Breed additive, dominance, and epistatic loss effects are of concern in the genetic evaluation of a multibreed population. Multiple regression equations used for fitting these effects may show a high degree of multicollinearity among predictor variables. Typically, when strong linear relationships exist, the regression coefficients have large SE and are sensitive to changes in the data file and...

The present article discusses the role of categorical variable in the problem of multicollinearity in linear regression model. It exposes the diagnostic tool condition number to linear regression models with categorical explanatory variables and analyzes how the dummy variables and choice of reference category can affect the degree of multicollinearity. Such an effect is analyzed analytically a...

JANMOHAMMADI MOHSEN, SABAGHNIA NASER, NOURAEIN MOJTABA. 2014. Path Analysis of Grain Yield and Yield Components and Some Agronomic Traits in Bread Wheat. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 62(5): 945–952. Development of new bread wheat cultivars needs effi cient tools to monitor trait association in a breeding program. This investigation was aimed to charact...

Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when t...

The evidence presented by Haas and Stack (1983) suggests that a parabolic relationship exists between a nation's level of industrialization and the strike activity among its labor force. Their model is tested using data from a different time period. Criticisms of the original model, including problems of heteroskedasticity, autocorrelation and multicollinearity, are addressed here. The findings...

in this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of multicolinearity. if one deletes the variable (s) that is (are) responsible for multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. to improve this method, we run the deleted va...

2. Various Methods of Estimation under Severe Multicollinearity Conditions: In what follows, we give a brief account of some important methods of estimation under severe multicollinearity conditions: (i). The Restricted Least Squares (RLS) Estimator of β : If we can put some restriction on the linear combination of regression coefficients such that , R r β = then the RLS estimator of β denoted ...