A NU test for serial correlation of residuals from one or more regression regimes

When measurements are made on units of production in time order, a problem of considerable importance is to test whether the random disturbances from a regression model are autocorrelated in time. Tool-wear processes provide an example that is important in itself and for which regression models can sometimes be useful in modeling and controlling the process. For these processes, because of compensator actions and direct interventions inter alia, the deterministic components of the data are frequently from regression regimes with different coefficients and variances, but the random components have the same autocorrelations. A test based on the normalized uniform (NU) residuals obtained from conditional probability integral transformations (CPIT) is given for testing for lag h autocorrelation of residuals from full-rank normal errors regression models. The exact distribution theory of these values permits the evaluation of the significance level for this test. Some limited power comparisons are made with th...

[1]  E. J. Hannan TESTING FOR SERIAL CORRELATION IN LEAST SQUARES REGRESSION , 1957 .

[2]  M. Bartlett An Inverse Matrix Adjustment Arising in Discriminant Analysis , 1951 .

[3]  A. Hedayat,et al.  Independent Stepwise Residuals for Testing Homoscedasticity , 1970 .

[4]  J. Durbin,et al.  Testing for serial correlation in least squares regression. II. , 1950, Biometrika.

[5]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[6]  A. J. Strecok,et al.  On the calculation of the inverse of the error function , 1968 .

[7]  G. Box,et al.  Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models , 1970 .

[8]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[9]  Donald F. Morrison,et al.  Applied linear statistical methods , 1983 .

[10]  R. Plackett Some theorems in least squares. , 1950, Biometrika.

[11]  J. Koerts,et al.  New Estimators of Disturbances in Regression Analysis , 1971 .

[12]  H. Theil The Analysis of Disturbances in Regression Analysis , 1965 .

[13]  Charles P. Quesenberry,et al.  An SPC Approach to Compensating a Tool-Wear Process , 1988 .

[14]  A. P. J. Abrahamse,et al.  A Comparison between the Power of the Durbin-Watson Test and the Power of the Blus Test , 1969 .

[15]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[16]  H. Theil Principles of econometrics , 1971 .

[17]  Layth C. Alwan,et al.  Time-Series Modeling for Statistical Process Control , 1988 .

[18]  C. Quesenberry,et al.  The Conditional Probability Integral Transformation and Applications to Obtain Composite Chi-Square Goodness-of-Fit Tests , 1973 .

[19]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[20]  Andrew Harvey,et al.  A Simple Test for Serial Correlation in Regression Analysis , 1974 .

[21]  J. Sargan,et al.  On the theory and application of the general linear model , 1970 .

[22]  Ray L. Marr,et al.  A Nu test for serial correlation of regression model residuals , 1989 .

[23]  J. R. Mcgregor An approximate test for serial correlation in polynomial regression , 1960 .

[24]  C. Quesenberry,et al.  Uniform Strong Consistency of Rao-Blackwell Distribution Function Estimators , 1972 .

[25]  Douglas M. Hawkins,et al.  The Use of Recursive Residuals in Checking Model Fit in Linear Regression , 1984 .