Testing for Block Effects in Regression Models Based on Survey Data

King, Maxwell L.; Evans, Merran

doi:10.1080/01621459.1986.10478320

Cited by 13 publications

(2 citation statements)

References 12 publications

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“…Breusch and Pagan show that this statistic, under H 0 , is asymptotically χ 2 1 as both n → ∞ and T → ∞. Honda (1985) and King and Evans (1986) observed that a ''one-sided'' Lagrange Multiplier test, based on the asymptotically standard normal distribution of…”

Section: Testing For Random Effects In Panel Datamentioning

confidence: 98%

Pseudo-Gaussian and rank-based optimal tests for random individual effects in large small panels

Bennala

Hallin

Paindaveine

2012

Journal of Econometrics

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Section: Testing For Random Effects In Panel Datamentioning

confidence: 98%

Pseudo-Gaussian and rank-based optimal tests for random individual effects in large small panels

Bennala

Hallin

Paindaveine

2012

Journal of Econometrics

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“…Often scientists are able to provide information regarding the signs of various parameters under test and this information can be used to improve the power of testing procedures. Examples occur in statistics (BHATTI and KING (1990), EVANS and KING (1985), FAREBROTHER (1986), SENGUPTA (1987SENGUPTA ( , 1988, KING and EVANS (1986), WOLAK (1987)), econometrics (BHATTI (1991), HILLIER (1987), JUDGE and YANCEY (1986), KING and SMITH (1986), KING (1987a), KING and W u (1990), WU and BHATTI (1989)) and biometrics (BHATTI (1990), SOLOMON (1986, 1988)). This paper re-examines the problem considered by Cox and SOLOMON (1988) which involves testing for a non-zero autocorrelation coefficient, e, when one has a number of very small samples.…”

Section: Introductionmentioning

confidence: 99%

Optimal Testing for Serial Correlation in a Large Number of Small Samples

Bhatti

1992

Biometrical J

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In recent years, there has been an increased awareness of the potential one-sided nature of many testing problems in applied sciences. Usually, these testing problems can be reduced, either by conditioning on suflicient statistics or by invariant techniques. Cox and SOLOMON (1988) considered testing the serial correlation coeflicient of a stationary first order autoregressive process and concentrated on four independent samples, with each of size three. We outline a general method for testing the serial correlation coeflicient, using locally best invariant, point optimal invariant and locally most mean powerful invariant test procedures. The first procedure optimizes power near the null hypothesis, the second optimizes it at a pre-determined point away from the null while the third optimizes the average curvature of the power hypersurface in the neighbourhood of the null hypothesis.

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On Testing for Equicorrelation in Random Coefficient Models

Bhatti¹,

Barry²

1995

Biometrical J

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summnryRecently Bmm (1993) considered an efficient estimation of random coefficient model based on survey data. The main objective of this paper is to construct one sided test for testing equicorrelation coefficient in presence of random coefficients using optimal testing procedure. The test statistic is a ratio of quadratic forms in normal variables which is most powerful and point optimal invariant.

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Testing for Block Effects in Regression Models Based on Survey Data

Cited by 13 publications

References 12 publications

Pseudo-Gaussian and rank-based optimal tests for random individual effects in large small panels

Pseudo-Gaussian and rank-based optimal tests for random individual effects in large small panels

Optimal Testing for Serial Correlation in a Large Number of Small Samples

On Testing for Equicorrelation in Random Coefficient Models

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