1,721,039 research outputs found
Design of double sampling rectifying OQ plans
No full textThis paper is a generalization of earlier study by Hall (1979), who introduced outgoing quality and total inspection as well defined random variables under total rectification. In this paper OQ attribute rectification sampling plans for double sampling is developed based on Venkatesan (2005). An extensive simulation study is presented to illustrate the determination of the plan
Remodelling Canadian Lynx Data
No full textWe re-examine the annual trappings of the Canadian Lynx over the years 1821-1934, which have been reported and analyzed extensively. This paper shows that the Full Range Autoregressive (FRAR) model, free from order determination processes, provides an acceptable alternative to the more widely adopted class of Autoregressive (AR) models
Bayesian Prediction of canadian lynx data using FRAR model
No full textARMA models are used as exploratory tools for identifying, estimating and forecasting for the Canadian lynx data, which have attained benchmark status in the time series literature since the work of Moran in 1953. This paper shows that the Full Range Autoregressive (FRAR) model, free from order determination processes, provides an acceptable alternative to the more widely adopted class of ARMA models
BAYESIAN INFERENCE TO MULTIPLE CHANGES IN THE VARIANCE OF AR(p) TIME SERIES MODEL
No full textThe problem of a change in the mean of a sequence of random variables at an unknown
time point has been addressed extensively in the literature. But, the problem of a change in the
variance at an unknown time point has, however, been covered less widely. This paper analyses a
sequence of autoregressive, AR(p), time series model in which the variance may be subjected to
multiple changes at an unknown time points. Posterior distributions are found both for the
unknown points of time at which the changes occurred and for the parameters of the model. A
numerical example is also discussed
New family of time series models and its bayesian analysis
No full textA new family of time series models, called the Full Range Autoregressive model, is introduced which avoids the difficult problem of order determination in time series analysis. Some of the basic statistical properties of the new model are studied. Further, the paper describes the Bayesian inference and forecasting as applied to the Full Range Autoregressive model. The Canadian lynx data is used to compare the efficiency of the predictive power of the new model with those of
some of the existing models in the time series literature
A bayesian analysis of multiple changes in the variance of first - order autoregressive time series models
No full textThe problem of a change in the mean of a sequence of random variables at an unknown time point has been addressed extensively in the literature. But, the problem of a change in the variance at an unknown time point has, however, been covered less widely. This paper analyses a sequence of first order autoregressive time series model in which the variance may have subjected to multiple changes at an unknown time points. Posterior distributions are found both for the unknown points of time at which the changes occurred and for the parameters of the model. A numerical example is illustrated
A Bayesian Analysis of Multiple Changes in the Variance of First-Order Autoregressive Time Series Models
No full textThe problem of a change in the mean of a sequence of random variables at an unknown time point has been addressed extensively in the literature. But, the problem of a change in the variance at an unknown time point has, however, been covered less widely. Hsu examines the problem of testing whether there has been a change in the variance at an unknown time point using sampling theory, and applies to stock return data and also give a Bayesian treatment of a similar problem.
This paper analyses a sequence of first order autoregressive time series model in which the variance may have subjected to multiple changes at an unknown time points. Posterior distributions are found both for the unknown points of time at which the changes occurred and for the parameters of the model. A numerical example is discussed
Bayesian analysis of change point problem in autoregressive model: a mixture model approach
No full textThis paper is a generalization of earlier studies by Venkatesan and Arumugam (2007) who considered the changes in the parameters of an autoregressive (AR) time series model in order to make Bayesian inference for the shift points and other parameters of a changing AR model. In this paper, the problem of gradual changes in the parameters of an AR model of pth order, through Bayesian mixture approach is considered. This model incorporates the beginning and end points of the interval of switch. Further, the Bayes estimates of the parameters are illustrated with the data generated from known model
- …
