This paper presnets a method for simultaneously estimating a system of nonparametric multiple regressions which may seem unrelated, but where the errors are potentially correlated between equations. We show that the prime advantage of estimating such a 'seemingly unrelated' system of...

In this paper, different approaches to dealing with nuisance parameters in the likelihood based inference are presented and illustrated by reference to the linear regression model with nonspherical errors. The estimator of the error variance using each of the approaches is also derived for the...

The segmentation problem arises in many applications in data mining, A.I. and statistics. In this paper, we consider segmenting simple time series. We develop two Bayesian approaches for segmenting a time series, namely the Bayes Factor approach, and the Minimum Message Length (MML) approach. We...

This paper estimates the long-term trends in the daily maxima of tropospheric ozone at six sites around the state of Texas. The statistical methodology we use controls for the effects of meteorological variables because it is known that variables such as temperature, wind speed and humidity...

Time series often have patterns that form a basis for comparing them or classifying them into groups. In this paper we present procedures to compare and classify stationary multivariate time series. Simulations studies show that the procedures perform fairly well for reasonably long series.

The concept of fractional cointegration, whereby deviations from an equilibrium relationship follow a fractionally integrated process, has attracted some attention of late. The extended concept allows cointegration to be associated with mean reversion in the error, rather than requiring the more...

A parsimonious method of exponential smoothing is introduced for time series generated from a combination of local trends and local seasonal effects. It is compared with the additive version of the Holt-Winters method of forecasting on a standard collection of real time series.