In recent years non-parametric density estimation has been extensively employed in several fields as a powerful descriptive tool, which is far more informative and robust than histograms. Moreover, the increased computation power of modern computers has made non-parametric density estimation a...

We discuss the application of the GHK simulation method to maximum likelihood estimation of the multivariate probit regression model, and describe and illustrate a Stata program mvprobit for this purpose.

Scientists often have good reasons for wanting to calculate multiple confidence intervals and/or p-values, especially when scanning a genome. However, if we do this, then the probability of not observing at least one "significant" difference tends to fall, even if all null hypotheses are true. A...

Margin is a user-written program that estimates average marginal effects, i.e. the sample average of the effects of partial or discrete changes in the explanatory variables. The presentation will compare the performance of margin and the official mfx. Margin is quicker because it computes the...

We develop FIML code for estimating a Poisson Count data model with lognormal unobserved heterogeneity and an endogenous dummy variable as proposed by Terza (1998). Gauss-Hermite quadrature is used for calculating the log-likelihood and a -ml d0- method is employed. We present an example and...

The talk illustrates a user-written command that extends the official kdensity to estimate density functions by the kernel method. The extensions are of two types. Firstly, the new command allows the use of an 'adaptive kernel' approach with varying, rather than fixed, bandwidths. Secondly,...

All doctors treating patients with Breast Cancer know which key variables indicate a good prognosis and which values decrease the chances of surviving. However because of complex interactions between the variables and survival doctors cannot give an individualized prognosis to a patient. The...

Missing imputation (MI) is a method to deal with missing at random (MAR) data. It is a Monte Carlo procedure where missing values are replaced by several (usually less than 10) simulated versions. It consists of three steps (Shafer, 1999): i. generation of the imputed values for the missing data;...

Most statistical data analysis, and thus most graphical data analysis, is directed towards modelling of relationships, but many statistical problems have a different flavour: their focus is comparison, and the key question is assessing agreement or disagreement between two or more data sets or...

A semi-nonparametric estimator is presented for a series of generalized models that nest the ordered probit model and thereby relax the distributional assumptions in that model. A new Stata command for the estimation of such models is presented. The approach is illustrated using examples.