Excellence in Research for Australia (ERA) is designed to provide a comprehensive review of the quality of research undertaken in Australian higher education institutions at regular intervals. The first ERA was conducted in 2010... more
Excellence in Research for Australia (ERA) is designed to provide a comprehensive review of the quality of research undertaken in Australian higher education institutions at regular intervals. The first ERA was conducted in 2010 (Australian Research Council, 2011a), the second will be conducted in 2012 and the third is planned for 2016. ERA was a successor to the Research Quality Framework (RQF) (DEST, 2005); an initiative prompted by political scepticism about the claims/assertions that universities made about the value of and returns on national invest-ment in research. In implementing ERA, Australia follows several other countries, including the United Kingdom
... Remark AS R24 A Remark on Algorithm AS 98: The Spectral Test for the Evaluation of Congruential Pseudo-random Generators By DAVID C. HOAGLIN and MAx L. KING Abt Associates Inc., Cambridge, Mass., USA, and University of Canterbury, ...
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ABSTRACT This paper investigates the application of the most mean powerful test to the problem of testing for heteroscedastic disturbances in the linear regression model. The most mean powerful test was introduced by the authors [Comput.... more
ABSTRACT This paper investigates the application of the most mean powerful test to the problem of testing for heteroscedastic disturbances in the linear regression model. The most mean powerful test was introduced by the authors [Comput. Stat. Data Anal. 49, 1097–1104 (2005)]] and is based on the generalized Neyman-Pearson lemma. This test provides an optimal test of certain composite hypotheses. Previous applications have only involved testing problems whose null hypotheses, after reduction through invariance arguments, are one-dimensional and two-dimensional [see the authors, J. Stat. Plann. Inference 134, No. 2, 536–548 (2005; Zbl 1066.62065)]. This is the first application involving the problem of testing heteroscedastic disturbances in linear regression models. A Monte Carlo simulation experiment was conducted to assess the small sample performance of the test with encouraging results.
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ABSTRACT This paper demonstrates the importance of taking care to find a global maximum when estimating nuisance parameters required for hypothesis tests. We find that sizes can be badly inflated when this is not done for a test of the... more
ABSTRACT This paper demonstrates the importance of taking care to find a global maximum when estimating nuisance parameters required for hypothesis tests. We find that sizes can be badly inflated when this is not done for a test of the general linear regression model.
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... approximation further. Suppose after attempting to construct a point optimal test, one is forced to consider an approximate point optimal test, and finds that, even after mini-mization, (9) is closer to a than to zero. Although the ...
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ABSTRACT We propose a simultaneous model specification procedure for the conditional mean and conditional variance in nonparametric and semiparametric time series econometric models. An adaptive and optimal model specification test... more
ABSTRACT We propose a simultaneous model specification procedure for the conditional mean and conditional variance in nonparametric and semiparametric time series econometric models. An adaptive and optimal model specification test procedure is then constructed and its asymptotic properties are investigated. The main results extend and generalize existing results for testing the mean of a fixed design nonparametric regression model to the testing of both the conditional mean and conditional variance nonparametric and semiparametric time series econometric models. In addition, we develop computer-intensive bootstrap simulation procedures for the selection of an interval of bandwidth parameters as well as the choice of asymptotic critical values. An example of implementation is given to show how to implement the proposed simultaneous model specification procedure in practice. Moreover, finite sample studies are presented to support the proposed test procedure
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... Kim-Leng Goh a & Maxwell King b * pages 751-759. ... The parameter space is partitioned into two sub-vectors , where the order of β is r×1. This article concerns testing the significance of β, in the presence of nuisance... more
... Kim-Leng Goh a & Maxwell King b * pages 751-759. ... The parameter space is partitioned into two sub-vectors , where the order of β is r×1. This article concerns testing the significance of β, in the presence of nuisance parameter γ. The hypotheses of interest are. ...
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... Examples can be found in papers by Zellner & Montmar-quette (1971), Rowley & Wilton (1973), and Kenward (1975). ... for r*. This may be done iteratively using either Koerts & Ab-rahamse's (1969) FQUAD subroutine,... more
... Examples can be found in papers by Zellner & Montmar-quette (1971), Rowley & Wilton (1973), and Kenward (1975). ... for r*. This may be done iteratively using either Koerts & Ab-rahamse's (1969) FQUAD subroutine, Farebrother's (1980) PAN pro-cedure or Davies' (1980 ...
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A significant role for hypothesis testing in econometrics involves diagnostic checking. When checking the adequacy of a chosen model, researchers typically employ a range of diagnostic tests, each of which is designed to detect a... more
A significant role for hypothesis testing in econometrics involves diagnostic checking. When checking the adequacy of a chosen model, researchers typically employ a range of diagnostic tests, each of which is designed to detect a particular form of model inadequacy. A major problem is how best to control the overall probability of rejecting the model when it is true and multiple test statistics are used. This paper presents a new multiple testing procedure, which involves checking whether the calculated values of the diagnostic statistics are consistent with the postulated model being true. This is done through a combination of bootstrapping to obtain a multivariate kernel density estimator of the joint density of the test statistics under the null hypothesis and Monte Carlo simulations to obtain a p value using this kernel density. We prove that under some regularity conditions, the estimated p value of our test procedure is a consistent estimate of the true p value. The proposed t...
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Page 1. Austral. J. Statist., 32(1), 1990, 87-97 A BETA-OPTIMAL TEST OF THE EQUICORRELATION COEFFICIENT MUHAMMAD I. BHATTI' AND MAXWELL L. KING' Monasli University Summary This paper considers the problem ...
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... King, Maxwell L., " Testing for Autocorrelation in Linear Regression Models: A Survey," forthcoming in Maxwell L. King and David EA Giles (eds.), Specification A nalysis in the Linear Model (London:Rutledge and Kegan... more
... King, Maxwell L., " Testing for Autocorrelation in Linear Regression Models: A Survey," forthcoming in Maxwell L. King and David EA Giles (eds.), Specification A nalysis in the Linear Model (London:Rutledge and Kegan Paul, 1987). King, Maxwell L., and Grant H. Hillier ...
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