Accession Number : ADA304006

Title :   Differential Geometrical Methods in Time Series.

Descriptive Note : Final rept. 15 Apr 91-14 Oct 95,

Corporate Author : CONNECTICUT UNIV STORRS

Personal Author(s) : Ravishanker, Nalini

PDF Url : ADA304006

Report Date : DEC 1995

Pagination or Media Count : 10

Abstract : Inference for time series processes was investigated using differential geometrical methods as well as sampling based Bayesian methods. For univariate autoregressive moving average (ARMA) processes and fractionally integrated ARMA processes analytical forms of asymptotic properties of inference such as bias in parameter estimates and improved test statistics were obtained from geometrical quantities. These terms provide collections useful when the sample size is moderate or small. Markov chain Monte Carlo procedures facilitated modeling of univariate and multivariate ARMA and fractionally integrated ARMA processes in the Bayesian framework. This approach uses the exact likelihood function and is accurate even with small sample sizes. Outlier analysis, prediction and model selection were addressed. (AN)

Descriptors :   *MATHEMATICAL MODELS, *STATISTICAL INFERENCE, *TIME SERIES ANALYSIS, *DIFFERENTIAL GEOMETRY, ALGORITHMS, PARAMETRIC ANALYSIS, MAXIMUM LIKELIHOOD ESTIMATION, MULTIVARIATE ANALYSIS, AUTOCORRELATION, MONTE CARLO METHOD, REGRESSION ANALYSIS, ASYMPTOTIC SERIES, SAMPLING, NONLINEAR ANALYSIS, COVARIANCE, BAYES THEOREM, STATISTICAL PROCESSES, MARKOV PROCESSES.

Subject Categories : Numerical Mathematics
      Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE