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Time-varying Coefficients (time-varying + coefficient)

Distribution by Scientific Domains
Mathematics and Statistics40%

Selected Abstracts

Testing for Granger (non-)causality in a time-varying coefficient VAR model

JOURNAL OF FORECASTING, Issue 4 2008
Dimitris K. Christopoulos
Abstract In this paper we propose Granger (non-)causality tests based on a VAR model allowing for time-varying coefficients. The functional form of the time-varying coefficients is a logistic smooth transition autoregressive (LSTAR) model using time as the transition variable. The model allows for testing Granger non-causality when the VAR is subject to a smooth break in the coefficients of the Granger causal variables. The proposed test then is applied to the money,output relationship using quarterly US data for the period 1952:2,2002:4. We find that causality from money to output becomes stronger after 1978:4 and the model is shown to have a good out-of-sample forecasting performance for output relative to a linear VAR model. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Time Deformation, Continuous Euler Processes and Forecasting

JOURNAL OF TIME SERIES ANALYSIS, Issue 6 2006
Chu-Ping C. Vijverberg
Abstract., A continuous Euler model has time-varying coefficients. Through a logarithmic time transformation, a continuous Euler model can be transformed to a continuous autoregressive (AR) model. By using the continuous Kalman filtering through the Laplace method, this article explores the data application of a continuous Euler process. This time deformation of an Euler process deforms specific time-variant (non-stationary) behaviour to time-invariant (stationary) data on the deformed time scale. With these time-invariant data on the transformed time scale, one may use traditional tools to conduct parameter estimation and forecasts. The obtained results can then be transformed back to the original time scale. Simulated data and actual data such as bat echolocation and the US residential investment growth are used to demonstrate the usefulness of time deformation in forecasting. The results indicate that fitting a traditional autoregressive moving-average (ARMA) model on an Euler data set without imposing time transformation leads to forecasts that are out of phase while the forecasts of an Euler model stay mostly in phase. [source]

Impact of hepatitis C virus infection and other comorbidities on survival in patients on dialysis

JOURNAL OF VIRAL HEPATITIS, Issue 10 2007
A. A. Butt
Summary., The impact of hepatitis C virus (HCV) and other comorbid conditions upon survival is not well quantified in patients on dialysis. We identified HCV-infected and uninfected persons in the USRDS using claims data in 1997,1998 and followed until September 22, 2002 or death. We used Gray's time-varying coefficients model to examine factors associated with survival. Subjects with a renal transplant were excluded. A total of 5737 HCV-infected and 11 228 HCV-uninfected persons were identified. HCV-infected subjects were younger (mean age 57.8 vs 65.3 years), more likely to be male (57.6%vs 49.6%) and black (54.0%vs 36.4%). They were more likely to have a diagnosis of drug (16.5%vs 4.6%) and alcohol use (14.0%vs 3.1%), and to be human immunodeficiency virus (HIV) co-infected (7.4%vs 1.8%) (all comparisons, P < 0.0005). In an adjusted Gray's time-varying coefficient model, HCV was associated with an increased risk of mortality (P < 0.0005). The hazards were highest at the time of HCV diagnosis and decreased to a stable level 2 years after diagnosis. Other factors associated with increased risk of mortality were (P < 0.0005 unless stated) HIV coinfection; diagnosis of drug use (P = 0.001); coronary artery disease (P = 0.006); stroke; diabetes as the primary cause for renal failure; peripheral vascular disease; depression and presence of anaemia. HCV was associated with higher risk of death in patients on dialysis, even after adjusting for concurrent comorbidities. The risk was highest at the time of HCV diagnosis and stabilized over time. Clinical trials of HCV screening and treatment to reduce mortality in this population are warranted. [source]

Estimation methods for time-dependent AUC models with survival data

THE CANADIAN JOURNAL OF STATISTICS, Issue 1 2010
Hung Hung
Abstract The performance of clinical tests for disease screening is often evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Recent developments have extended the traditional setting to the AUC with binary time-varying failure status. Without considering covariates, our first theme is to propose a simple and easily computed nonparametric estimator for the time-dependent AUC. Moreover, we use generalized linear models with time-varying coefficients to characterize the time-dependent AUC as a function of covariate values. The corresponding estimation procedures are proposed to estimate the parameter functions of interest. The derived limiting Gaussian processes and the estimated asymptotic variances enable us to construct the approximated confidence regions for the AUCs. The finite sample properties of our proposed estimators and inference procedures are examined through extensive simulations. An analysis of the AIDS Clinical Trials Group (ACTG) 175 data is further presented to show the applicability of the proposed methods. The Canadian Journal of Statistics 38:8,26; 2010 © 2009 Statistical Society of Canada La performance des tests cliniques pour le dépistage de maladie est souvent évaluée en utilisant l'aire sous la courbe caractéristique de fonctionnements du récepteur (, ROC , ), notée , AUC , . Des développements récents ont généralisé le cadre traditionnel à l'AUC avec un statut de panne binaire variant dans le temps. Sans considérer les covariables, nous commençons par proposer un estimateur non paramétrique pour l'AUC simple et facile à calculer. De plus, nous utilisons des modèles linéaires généralisés avec des coefficients dépendant du temps pour caractériser les AUC, dépendant du temps, comme fonction des covariables. Les procédures d'estimation asociées correspondantes sont proposées afin d'estimer les fonctions paramètres d'intérêt. Les processus gaussiens limites sont obtenus ainsi que les variances asymptotiques estimées afin de construire des régions de confiance approximatives pour les AUC. À l'aide de nombreuses simulations, les propriétés pour de petits échantillons des estimateurs proposés et des procédures d'inférence sont étudiées. Une analyse du groupe d'essais cliniques sur le sida 175 (ACTG 175) est aussi présentée afin de montrer l'applicabilité des méthodes proposées. La revue canadienne de statistique 38: 8,26; 2010 © 2009 Société statistique du Canada [source]

OPEN-LOOP AND CLOSED-LOOP OPTIMIZATION OF LINEAR CONTROL SYSTEMS

ASIAN JOURNAL OF CONTROL, Issue 3 2000
R. Gabasov
ABSTRACT A canonical optimal control problem for linear systems with time-varying coefficients is considered in the class of discrete controls. On the basis of linear programming methods, two primal and two dual methods of constructing optimal open-loop controls are proposed. A method of synthesis of optimal feedback control is described. Results are illustrated by a fourth-order problem; estimates of efficiency of proposed methods are given. [source]