We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We validate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformatics context of proteomics data on the interactome graph. We make all our experi...

For the most part, this book concerns itself with measurement data and the corresponding analyses based on normal distributions. In this chapter and the next we consider data that consist of counts. Elementary count data were introduced in Chapter 5. Frequently data are collected on whether or not a certain event occurs. A mouse dies when exposed to a dose of chloracetic acid or it does not. In...

©FSRH J Fam Plann Reprod Health Care 2008: 34(3) What is it? When a response variable has only two possible values (e.g. recurrence/not), binary logistic regression is commonly used to test or model the association between that response and a number of potential explanatory variables, with each association estimated in terms of an odds ratio (OR). Multinomial logistic regression is an extension...

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the associati...

objective: to examine the relationship between socioeconomic factors affecting contraceptive use among tribal women of bangladesh with focusing on son preference over daughter. materials and methods: the study used data gathered through a cross sectional survey on four tribal communities resided in the rangamati hill district of the chittagong hill tracts, bangladesh. a multistage random sampli...

this paper defines how a meta heuristic search engine called p.s.o can be used to maximize the objective function of a logistic regression model, describing the relationship between the response variable (product designs' score) and a set of explanatory variables (product design factors). at the first phase the processed data, classified and categorized, by kansie engineering is used as input t...

logistic regression (lr) was used to model urban growth between the years 1987 and 2001 in gorgan city, north east of iran. three groups of variables including economic-social, land use and biophysical variables were used in the modeling practice. using covariance of the independent variables, distance to administrative and sporting centers plus distance to cities were removed. roc (relative op...

Communication decisions in networks can be described as a two-level decision process. The second decision about event receivers is a multinomial logistic regression model with an unknown vector of parameters. These parameters evaluate network structures that enforce or weaken the probability for choosing certain actors. However, in many cases those parameters may change over time. In this paper...

When the choice of the nominal anchor and timing of inflation stabilization is analyzed with models of political business cycles, there is room for political opportunism of policymakers. The different business cycles associated with exchange rate-based (ERBS) and money-based stabilizations (MBS) imply that the decision regarding the timing and nominal anchor of stabilization may be affected by ...