Logistic regression techniques can be used to restrict the conditional probabilities of a Bayesian network for discrete variables. More specifically, each variable of the network can be modeled through a logistic regression model, in which the parents of the variable define the covariates. When all main effects and interactions between the parent variables are incorporated as covariates, the co...

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

Abstr act Simulations or mathematical analysis of a real-world logistic network require a model. In this context two challenges occur for modelling: First, the model should represent the real-world logistic network in an accurate way. Second, it should foster simulations or analytical analysis to be conducted in a reasonable time. A large size is often a drawback of many models. In the case of ...

Though the Clean Air Act (CAA) relies on a traditional inter-government partnership, new initiatives from local governments that fall outside of the conventional implementation strategy have created a unique public service delivery network by adding layers to the implementation scheme. Using both logistic and multinomial logistic models and a dataset of 497 Metropolitan (MSA) and Micropolitian ...

BACKGROUND The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. METHODS Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and...

This paper proposes an adaptive technique in the prediction of dichotomous response variable by combining fuzzy concept with statistical logistic regression. The model was tested on cancer dataset in predicting cancer susceptibility. In this paper we will present the development, evaluation and validation of the proposed model based on the experiment carried out. Explanatory power of the adapti...

The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead...