Random walks - a sequential approach

In this paper sequential monitoring schemes to detect nonparametric drifts are studied for the random walk case. The procedure is based on a kernel smoother. As a by-product we obtain the asymptotics of the Nadaraya-Watson estimator and its as- sociated sequential partial sum process under non-standard sampling. The asymptotic behavior differs substantially from the stationary situation, if there is a unit root (random walk component). To obtain meaningful asymptotic results we consider local nonpara- metric alternatives for the drift component. It turns out that the rate of convergence at which the drift vanishes determines whether the asymptotic properties of the monitoring procedure are determined by a deterministic or random function. Further, we provide a theoretical result about the optimal kernel for a given alternative

A procedure for the change point problem in parametric models based on phi-divergence test-statistics

This paper studies the change point problem for a general parametric, univariate or multivariate family of distributions. An information theoretic procedure is developed which is based on general divergence measures for testing the hypothesis of the existence of a change. For comparing the accuracy of the new test-statistic a simulation study is performed for the special case of a univariate discrete model. Finally, the procedure proposed in this paper is illustrated through a classical change-point example

Community food program use in Inuvik, Northwest Territories

Background: Community food programs (CFPs) provide an important safety-net for highly food insecure community members in the larger settlements of the Canadian Arctic. This study identifies who is using CFPs and why, drawing upon a case study from Inuvik, Northwest Territories. This work is compared with a similar study from Iqaluit, Nunavut, allowing the development of an Arctic-wide understanding of CFP use – a neglected topic in the northern food security literature. Methods: Photovoice workshops (n=7), a modified USDA food security survey and open ended interviews with CFP users (n=54) in Inuvik. Results: Users of CFPs in Inuvik are more likely to be housing insecure, female, middle aged (35–64), unemployed, Aboriginal, and lack a high school education. Participants are primarily chronic users, and depend on CFPs for regular food access. Conclusions: This work indicates the presence of chronically food insecure groups who have not benefited from the economic development and job opportunities offered in larger regional centers of the Canadian Arctic, and for whom traditional kinship-based food sharing networks have been unable to fully meet their dietary needs. While CFPs do not address the underlying causes of food insecurity, they provide an important service for communities undergoing rapid change, and need greater focus in food policy herein

Retrospective change detection for binary time series models

Detection of changes in health care performance, financial markets, and industrial processes have recently gained momentum due to the increased availability of complex data in real-time. As a consequence, there has been a growing demand in developing statistically rigorous methodologies for change-point detection in various types of data. In many practical situations, the data being monitored for the purpose of detecting changes are autocorrelated binary time series. We propose a new statistical procedure based on the partial likelihood score process for the retrospective detection of change in the coefficients of a logistic regression model with AR(p)-type autocorrelations. We carry out some Monte Carlo experiments to evaluate the power of the detection procedure as well as its probability of false alarm (type I error). We illustrate the utility using data on 30-day mortality rates after cardiac surgery and to data on IBM share transactions