Logistic regression is a technique to map the input feature to the posterior probability for a binary class. The optimal parameter of regression function is obtained by maximizing log likelihood of training data. In this report, we implement two optimization techniques 1) stochastic gradient decent (SGD); 2) limited-memory BroydenFletcherGoldfarbShanno (L-BFGS) to optimize the log likelihood fu...

Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This applied greatly business enterprises government sectors delivering the services to their customers, clients citizens. But, interaction successful only based on trust that each device has another. Thus very much essential network. As have access over sensi...

Logistic regression and linear discriminant analysis are used to estimate probability of success for binary data based on a training sample and a certain amount of prior information. Posterior probabilities of success are calculated for different choices of the model for the training sample distribution. An approach to model selection is suggested for certain classes of distributions based on t...

In most of advanced processes, quality of a final product depends on the several quality characteristic in the previous stages. This is called a cascade property in multi-stage processes. On the other hand, sometimes the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variable(s).This relation is called profile. In some ...

There is a known connection between the multinomial and the Poisson likelihoods. This, in turn, means that a Poisson regression may be transformed into a logit model and vice versa. In this paper, I show the data transformations required to implement this transformation. Several examples are used as illustrations.

Background: Due to the importance of medical studies, researchers of this field should be familiar with various types of statistical analyses to select the most appropriate method based on the characteristics of their data sets. Classification and regression trees (CARTs) can be as complementary to regression models. We compared the performance of a logistic regression model and a CART in predi...

background: although the cox proportional hazard regression is the most popular model for analyzing the prognostic factors on survival of cancer patients, under certain circumstances, parametric models estimate the parameter more efficiently than the cox model.  the aim of this study was to compare the cox regression model  with parametric models in patients with gastric cancer who registered a...