Add to ORKGThe famous p* formula provides a higher-order approximation for the conditional distribution of the maximum likelihood estimator given an exact ancillary. By collating well known existing results including application of a formal higher-order expansion the formula is generalized for the case of approximate ancillaries. Applied to the class of autoregressive models a case for conditional inference is made and the accuracy of the resulting approximation is demonstrated numerically
For modeling count time series data, one class of models is generalized integer autoregressive of or...
Testing for first-order autocorrelation in small samples using the standard asymptotic test can be s...
The computation of the likelihood function and the term structure of probabilistic forecasts in high...
The famous p* formula provides a higher-order approximation for the conditional distribution of the ...
We are interested in the probability that the maximal value of a stochastic process exceeds a value ...
For testing canonical parameters in a continuous exponential family, P-values based on higher order ...
This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptoti...
The quasi-maximum likelihood estimator for the autoregressive parameter in a spatial autoregression...
The extension of exact conditional inference to include so-called approximate conditional methods ha...
The extension of exact conditional inference to include so-called approximate conditional methods ha...
I consider parametric models with a scalar parameter of interest and multiple nuisance parameters. T...
We extend PML theory to account for information on the conditional moments up to order four, but wit...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
We extend PML theory to account for information on the conditional moments up to order four, but wit...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
For modeling count time series data, one class of models is generalized integer autoregressive of or...
Testing for first-order autocorrelation in small samples using the standard asymptotic test can be s...
The computation of the likelihood function and the term structure of probabilistic forecasts in high...
The famous p* formula provides a higher-order approximation for the conditional distribution of the ...
We are interested in the probability that the maximal value of a stochastic process exceeds a value ...
For testing canonical parameters in a continuous exponential family, P-values based on higher order ...
This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptoti...
The quasi-maximum likelihood estimator for the autoregressive parameter in a spatial autoregression...
The extension of exact conditional inference to include so-called approximate conditional methods ha...
The extension of exact conditional inference to include so-called approximate conditional methods ha...
I consider parametric models with a scalar parameter of interest and multiple nuisance parameters. T...
We extend PML theory to account for information on the conditional moments up to order four, but wit...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
We extend PML theory to account for information on the conditional moments up to order four, but wit...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
For modeling count time series data, one class of models is generalized integer autoregressive of or...
Testing for first-order autocorrelation in small samples using the standard asymptotic test can be s...
The computation of the likelihood function and the term structure of probabilistic forecasts in high...