Confidence Distributions for the Autoregressive Parameter

Confidence Intervals for Autoregressive Coefficients Near One

Often we are interested in the largest root of an autoregressive process. Available methods rely on inverting t-tests to obtain confidence intervals. However, for large autoregressive roots, t-tests do not approximate asymptotically uniformly most powerful tests and do not have optimality properties when inverted for confidence intervals. We exploit the relationship between the power of tests a...

Confidence interval for the two-parameter exponentiated Gumbel distribution based on record values

In this paper, we study the estimation problems for the two-parameter exponentiated Gumbel distribution based on lower record values. An exact confidence interval and an exact joint confidence region for the parameters are constructed. A simulation study is conducted to study the performance of the proposed confidence interval and region. Finally, a numerical example with real data set is gi...

Parameter estimation for non-Gaussian autoregressive processes

It is proposed to jointly estimate the parameters of nonGaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is iid as a two component Gaussian sum mixture. The result...

Parameter Estimates for Fractional Autoregressive Spatial Processes

A binomial-type operator on a stationary Gaussian process is introduced in order to model long memory in the spatial context. Consistent estimators of model parameters are demonstrated. In particular , it is shown thatˆdN − d = OP ((Log N) 3 N), where d = (d1, d2) denotes the long memory parameter.

Confidence Intervals for the Binomial Parameter