In this article, we introduce a new Stata command, smultiv, that implements the S-estimator of multivariate location and scatter. Using simulated data, we show that smultiv outperforms mcd, an alternative robust estimator. Finally, we use smultiv to perform robust principal component analysis...

We propose a semi-parametric mode regression estimator for the case in which the dependent variable has a continuous conditional density with a well-defined global mode. The estimator is semi-parametric in that the conditional mode is specified as a parametric function, but only mild assumptions...

We discuss the robust estimation of a linear trend if the noise follows an autoregressive process of first order. We find the ordinary repeated median to perform well except for negative correlations. In this case it can be improved by a Prais-Winsten transformation using a robust...

We discuss the robust estimation of a linear trend if the noise follows an autoregressive process of first order. We find the ordinary repeated median to perform well except for negative correlations. In this case it can be improved by a Prais-Winsten transformation using a robust...

Statistical data sets often contain observations that differ markedly from the bulk of the data. These outlying observations, ‘outliers’, have given rise to notable risks for statistical analysis and inference. Unfortunately, many of the classical statistical methods, such as ordinary least...

This study determines the relative effects of price, cattle quality, and feeding performance factors on profit per head for fed cattle marketed via a grid structure. Two different data sets of cattle that were marketed in two different grid pricing systems are used in the analysis with...

In this paper we develop flexible techniques for measuring the speed of output convergence between countries when such convergence may be of an unknown non-linear form. We then calculate these convergence speeds for various countries, in terms of half-lives, from two time-series data-sets. These...

A regression estimator is said to be robust if it is still reliable in the presence of outliers. On the other hand, its standard error is said to be robust if it is still reliable when the regression errors are autocorrelated and/or heteroskedastic. This paper shows how robust standard errors...