the work exposure conditions such as dust concentration, exposure time, use of respiratory protection devices and smoking status are effective to cause pulmonary function disorder. the objective of this study was prediction of pulmonary disorders in workers exposed to silica dust using artificial neural networks and logistic regression. a sample of 117 out of 150 workers employed in the stone c...

In this project, we study learning the Logistic Regression model by gradient ascent and stochastic gradient ascent. Regularization is used to avoid overfitting. Some practical tricks to improve learning are also explored, such as batch-based gradient ascent, data normalization, grid searching, early stopping, and model averaging. We observe the factors that affect the result, and determine thes...

• In this paper, we introduce a generalized skew logistic distribution that contains the usual skew logistic distribution as a special case. Several mathematical properties of the distribution are discussed like the cumulative distribution function and moments. Furthermore, estimation using the method of maximum likelihood and the Fisher information matrix are investigated. Two real data applic...

Niyogi and Berwick have developed a deterministic dynamical model of (E-)language change from which they analytically derive logistic, S-shaped spread of a linguistic variant through a speech community given certain assumptions about the language learning algorithm, the linguistic environment, and so forth. I will demonstrate that the same assumptions embedded in a stochastic model of (E-)langu...

Investigation for using different penalty functions (L1 absolute value penalty or lasso, L2 standard weight decay or ridge regression, weight elimination etc.) on the weights for logistic regression for classification. 5 data sets from UCI Machine Learning Repository were used.

In a regression model, the joint distribution for each finite sample of units is determined by a function px.y/ depending only on the list of covariate values xD .x.u1/,. . .,x.un// on the sampled units. No random sampling of units is involved. In biological work, random sampling is frequently unavoidable, in which case the joint distribution p.y,x/ depends on the sampling scheme. Regression mo...

A frequent problem in estimating logistic regression models is a failure of the likelihood maximization algorithm to converge. In most cases, this failure is a consequence of data patterns known as complete or quasi-complete separation. For these patterns, the maximum likelihood estimates simply do not exist. In this paper, I examine how and why complete or quasi-complete separation occur, and ...

Logistic regression is frequently used for classifying observations into two groups. Unfortunately there are often outlying observations in a data set, who might affect the estimated model and the associated classification error rate. In this paper, the effect of observations in the training sample on the error rate is studied by computing influence functions. It turns out that the usual influe...

Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm...