The problem of predicting 0-1-events is considered under general conditions, including stationary processes with short and long memory as well as processes with changing distribution patterns. Nonparametric estimates of the probability function and prediction intervals are obtained.

The problem of predicting 0-1-events is considered under general conditions, including stationary processes with short and long memory as well as processes with changing distribution patterns. Nonparametric estimates of the probability function and prediction intervals are obtained.

SEMIFAR models introduced in Beran (1999) provide a semiparametric modelling framework that enables the data analyst to separate deterministic and stochastic trends as well as short- and long-memory components in an observed time series. A correct distinction between these components, and in...

In this paper, we analyze the problem of congestion and quality loss of data transmission through the Internet from an economic perspective. We show that due to the congestion problem, quality sensitive services are likely to be crowded out by high volume but less quality sensitive applications...

This paper applies a local-linear non-parametric kernel regression technique to examine the effect of macroeconomic factors on stock market performance in Ghana. We show that the popular parametric specification in the existing literature suffers from functional misspecification. The evidence...