This is a preview. Log in through your library . Abstract Objectives: Poisson regression is now widely used in epidemiology, but researchers do not always evaluate the potential for bias in this ...

It has long been known that insufficient consideration of spatial autocorrelation leads to unreliable hypothesis-tests and inaccurate parameter estimates. Yet, ecologists are confronted with a ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

Beside the model, the other input into a regression analysis is some relevant sample data, consisting of the observed values of the dependent and explanatory variables for a sample of members of the ...

2. FHH a program for estimation of discrete time duration models with group-level heterogeneity Honoré, Bo E. (1992): "Trimmed Lad and Least Squares Estimation of Truncated and Censored Regression ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

Dr. James McCaffrey of Microsoft Research uses code samples, a full C# program and screenshots to detail the ins and outs of kernal logistic regression, a machine learning technique that extends ...