Abstract not found

This paper proposes and develops a nonparametric statistical procedure for estimating linear trend effect on data using nonparametric regression methods based on ranks, assuming one of the sampled populations is a measurement on a time scale. The populations of interest may be measurements

Abstract We construct and investigate a consistent kernel-type nonparametric esti-mator of the intensity function of a cyclic Poisson process in the presence of linear trend. It is assumed that only a single realization of the Poisson process is observed in a bounded window. We prove

has k unit roots and n- k distinct stationary linear combinations. Our proposed tests can be viewed alternatively as tests of the number of common trends, linearly independent cointegrating vectors, or autoregressive unit roots of the vector process. Both of the proposed tests are asymptotically

In this paper, we develop a process control approach to detect linear trends in the process mean. A statistic based on the deviation between the target mean and the expected mean of the process is used in the development of the new approach. The statistic is shown to have a Chi-square distribution

[1] Quality controlled oceanographic profile salinity

Tests based on rank statistics are introduced to test for systematic changes in a sequence of independent observations. Proposed tests include a rank test analogous to the parametric likelihood ratio test and others analogous to parametric Bayes tests. The tests are usable with either one- or twosided alternative hypotheses, and their asymptotic distributions are studied. The results of the general model are applied to two special cases, and their asymptotic distributions are also investigated. A Monte Carlo study verifies the applicability of asymptotic critical points in samples of moderate size, and other simulation studies compare power of the competing tests and their special-case versions. Finally, these tests are applied to a data set of traffic fatalities. 1. INTRODUCTION The situation which will be considered in this paper, following the work of Lombard (1987) is that of a set of independent and ordered random variables Y 1 ; Y 2 ; : : : ; Y T with continuous distribution fu...

We consider bivariate regressions of nonstationary fractionally integrated variables dominated by linear time trends. We first analyze the limiting behavior of the OLS estimators and prove that limiting normality arises at a faster rate of convergence than if the individual series were detrended

Abstract The proposed model considers the products with finite shelf-life which causes a small amount of decay. The market demand is assumed to be level dependent and in a linear form. The model has also considered the constant production rate which stops attaining a desired level of inventories

. The resulting designs are referred to as trend-free designs. In factorial experiments, the experimental units may be affected by the trend effect over time. The trend effect may be linear, quadratic or higher order. Here we consider the linear trend effect in the experimental units. Different methods