to investigate the effects of certain factors on the rate of retained placenta, 2844 calving records from 1288 holstein cows in a herd were used. these cows calved during year period of 2001 to 2007. a generalized statistical linear model was applied to analyze the data. logistic regression model was applied as the statistical model. in the model, fixed effects of year, season (warm or cold) an...

When response outcomes are continuous error terms in models are normally distributed and a standard normal distribution function is adequate. The logistic distribution function which is very similar to the normal distribution function is required when the response variable is binary. Parameters of a logistic response function are often estimated using the method of maximum likelihood (ML). One ...

this article deals with the relationship existing between the emotional aspect and decision-making processes. more specifically, it examines the links between emotional intelligence, decision biases and effectiveness of the governance mechanisms. the primary purposes of this article are to: consider emotional intelligence like new research ideas that make important contributions to society; off...

SIMULATING DEPENDENT BINARY DATA WITH RANDOM EFFECTS. Aobo Wang, Roy T. Sabo, Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia 23298-0032. Dependent binary data can be simply simulated using the multinomial sampling method. We extend this method to simulate dependent binary data with clustered random effect structures. Several distributions are considered for co...

A distance-based conditional model on the ranking poset is presented for use in classification and ranking. The model is an extension of the Mallows model, and generalizes the classifier combination methods used by several ensemble learning algorithms, including error correcting output codes, discrete AdaBoost, logistic regression and cranking. The algebraic structure of the ranking poset leads...

BACKGROUND Brain-body associations are essential in influencing outcome in patients with ruptured brain aneurysms. Thus far, there is scarce literature on such important relationships. METHODS The multicenter Tirilazad database (3551 patients) was used to create this clinical outcome prediction model in order to elucidate significant brain-body associations. Traditional binary logistic regres...

the extensive use of traditional irrigation systems has led to overexploitation of groundwater and overuse of surface water in theurmia  lake catchment (ulc) area ofiran. the purpose of this study was to model the adoption process of drip irrigation system (dis) by apple orchardists (aos) using the five stages of roger’s model for innovation decision process (idp). survey method of applying que...

Learning the distribution of natural images is one of the hardest problems in machine learning. We break down this challenging problem by mapping images into a hierarchy of binary images (bit-planes). In this representation, the top bitplane is critical, having fluctuations in structures over a vast range of scales. The ones below go through a gradual stochastic heating process to disorder. We ...

https://rde.ac Logistic regression is a regression model where the dependent variable is categorical and corresponding independent variables can be categorical or continuous. This article covers the case of a binary dependent variable such as an event occurring coded 1 = ‘event’ and 0 = ‘no event’. Frequent outcomes are pass/fail, win/lose, disease/no disease, etc. The logistic regression model...

We propose a logistic regression method based on the hybridation of a linear model and product-unit neural network models for binary classification. In a first step we use an evolutionary algorithm to determine the basic structure of the product-unit model and afterwards we apply logistic regression in the new space of the derived features. This hybrid model has been applied to seven benchmark ...