After the Shewhart method was suggested for industrial applications, other applications such as surveillance for bioterrorism and financial transactions have come into focus. Also other methods for surveillance have followed. The relation between the Shewhart method and the followers is examined. A uniform presentation of methods, by expressions of likelihood ratios, facilitates the comparisons...

This paper presents an adaptive Generalized Likelihood Ratio (GLR) test for multiple Faults Detection and Isolation (FDI) in stochastic linear dynamic systems. Based on the work of Willsky and Jones (1976), we propose a modified generalized likelihood ratio test, allowing detection, isolation and estimation of multiple sequential faults. Our contribution aims to maximise the good decision rate ...

ii c 2013 Yi Liu ALL RIGHTS RESERVED iii ABSTRACT YI LIU. Generalized Quasi-Likelihood Ratio Statistics for Multivariate Time-varying Coefficient Regression Models. (Under the direction of DR. JIANCHENG JIANG) Generalized likelihood ratio statistics have been a generally applicable method for testing nonparametric hypotheses about nonparametric functions.It has been widely used in many research...

Time-varying coefficient models are useful in longitudinal data analysis. Various efforts have been invested for the estimation of the coefficient functions, based on the least squares principle. Related work includes smoothing spline and kernel methods among others, but these methods suffer from the shortcoming of non-robustness. In this paper, we introduce a local M-estimation method for esti...

The Generalized Poisson Distribution (GPD) adds an extra parameter to the usual Poisson distribution. This parameter induces a loss of homogeneity in the stochastic processes modeled by the distribution. Thus, the generalized distribution becomes an useful model for counting processes where the occurrence of events is not homogeneous. This model creates the need for an inferential procedure, to...

Using many moment conditions can improve efficiency but makes the usual GMM inferences inaccurate. Two step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and under ...

We propose goodness of t tests for testing generalized linear models and semiparametric regression models against smooth alternatives The focus is on models having both continuous and factorial covariates As smooth extension of a parametric or semiparametric model we use generalized varying coe cient models as proposed by Hastie Tibshirani A likelihood ratio statistic is used for testing and as...

In structural health monitoring (SHM) and control, the structure can be instrumented with a redundant sensor network, which can be utilized in sensor fault diagnosis. In this study, the objective is to detect, identify, and quantify a sensor fault using the structural response data from the sensor network. Seven different sensor fault types are investigated and modelled: bias, gain, drifting, p...

Vegetation productivity metrics, such as gross primary production (GPP) may be determined from the efficiency with which light is converted into photosynthates, or light use efficiency (ε). Therefore, accurate measurements and modeling of ε is important for estimating GPP in each ecosystem. Previous studies have quantified the impacts of biophysical parameters on light use efficiency based GPP ...

In this paper, we propose a goodness-of-fit test based on the empirical likelihood method for the generalized lambda distribution (GLD) family. Such a nonparametric test approximates the optimal Neyman-Pearson likelihood ratio test under the unknown alternative distribution scenario. The p-value of the test is approximated through the simulations due to the dependency of the test statistic on t...