Comparative performance of tests of normality in detecting mixtures of parallel regression lines

References

• Aggarwal , L.K. 1981 . An exploratory method for the classification of social-political-economic entities for use in regression . Proceedings of the 13th Annual Meeting American Institute for Decision Sciences , 413
   Google Scholar
• Aggarwal , L.K. 1985 . An exploratory procedure for tracking heterogeneity associated with the intercept in regression . Educational Psychological Measurement , 45 : 753 – 762 .
   Google Scholar
• Anderson , T.W. and Darling , D.A. 1954 . A test of goodness of fit . Journal of the American Statistical Association , 49 : 765 – 69 .
   Web of Science ®Google Scholar
• Chen , E.H. 1971 . The power of the Shapiro-Wilk test for normality in samples from contaminated normal distributions . journal of the American Statistical Association , 66 : 760 – 769 .
   Web of Science ®Google Scholar
• D'Agostino , R.B. and Pearson , E.S. 1973 . Tests for departure from normality. Fuller empirical results for the distributions of b2 and Vf?1/ . Biometrika , 60 : 613 – 22 .
   Web of Science ®Google Scholar
• Eisenberger , I. 1964 . Genesis of bimodal distributions . Technometrics , 6 : 357 – 363 .
   Web of Science ®Google Scholar
• Franke , R. and Jayachandran , T. 1983 . An empirical investigation of the properties of a new goodness-of-fit test . Journal of Statistical Computation and Simulation , 16 : 175 – 187 .
   Web of Science ®Google Scholar
• Giles , D.E.A. and Hampton , P. 1983 . Urban migration in New Zealand--dummy variable interpretations . Journal of Urban Economics , 14 : 124 – 125 .
   Google Scholar
• Gordon , N.M. , Morton , T.E. and Braden , I.C. 1974 . salaries: Is there discrimination by sex, race, and discipline? . The American Econoimic Review , 64 : 419 – 427 .
   Web of Science ®Google Scholar
• Hocking , R.R. 1976 . The analysis and selection of variables in linear regression . Biometrics , 32 : 1 – 49 .
   Web of Science ®Google Scholar
• Huang , C.J. , Ben , W. and Bolch . 1974 . 0n the testing of regression disturbances for normality . Journal of the American Statistical Association , 69 : 330 – 335 .
   Web of Science ®Google Scholar
• Kiefer , N. 1978 . Discrete parameter variation: Efficient estimation of a switching regression model 1 . Econometrica , 46 : 427 – 34 .
   Web of Science ®Google Scholar
• Lillefors , H.J. 1967 . On the kolmogorov-smirnov tests for normality with mean and variance unknown . Journal of the American Statistical Association , 62 : 399 – 402 .
   Google Scholar
• Martinez , J. and Iglewicz , B. 1981 . A test for departure from normality based on a biweight estimator of scale . Biometrica , 68 : 331 – 3 .
   Web of Science ®Google Scholar
• McGee , V.P.S. and Carleton , W.T. 1970 . Piecewise regression . Journal of the American Statistical Association , 65 : 1109 – 1124 .
   Web of Science ®Google Scholar
• Meter , J. , Wasserman , W. and Kuttner , A. 1985 . Applied Linear Statistical Models , Homewood : Irwin .
   Google Scholar
• 0'Bryant , S.L. and Wolf , S.M. 1983 . Explanations of housing satisfaction of older homeowners and renters . Reasearch on Aging , 5 : 217 – 233 .
   Google Scholar
• Pallas , A.M. and Alexander , K.L. 1983 . Sex differences in quantitative SAT performance; new evidence on the differential coursework hypothesis . American,Educational Research Journal , 20 : 165 – 182 .
   Web of Science ®Google Scholar
• Pearson , E.S. , D'Agostino , R.B. and Bowman , K.O. 1977 . Tests for departure for normality^ comparison of powers . Biometrica , 64 : 231 – 46 .
   Web of Science ®Google Scholar
• Pierce , D.A. and Gray , R.J. 1982 . Testing normality of errors in regression models . Biometrica , 69 : 233 – 6 .
   Google Scholar
• Poiissar , L. and Diehr , P. 1982 . Regression-analysis in health-services research—the use of dummy variables . Medical Care , 20 : 959 – 966 .
   PubMedGoogle Scholar
• Quandt , R.E. 1972 . A new approach to estimating switching regressions . Journal of the American Statistical Association , 67 : 306 – 310 .
   Web of Science ®Google Scholar
• Quandt , R.E. and Ramsey , J.B. 1978 . Estimating mixtures of normal distributions and switching regressions . Journal of the American Statistical Association , 73 : 730 – 738 .
   Web of Science ®Google Scholar
• Shapiro , S.S. and Francia , R.S. 1972 . An approximate analysis of variance test for normality . Journal of the American Statistical Association , 67 : 215 – 216 .
   Web of Science ®Google Scholar
• Shapiro , S.S. and Wilk , M.B. 1965 . An analysis of variance test for normality (complete samples) . Biometrica , 52 : 591 – 611 .
   Web of Science ®Google Scholar
• Shapiro , S.S. , Wilk , M.B. and Chen , H.J. 1968 . A comparative study of various tests for normality . Journal of the American Statistical Association , 63 : 1343 – 1372 .
   Web of Science ®Google Scholar
• Shih , S.F. and Shin , W.F.P. 1978 . Use of dummy variables in water-resources studies . Journal of Hydrology , 38 : 289 – 298 .
   Google Scholar
• Smith , V.K. 1975 . A simulation analysis of the power of several tests for detecting heavy-tailed distributions . Journal of the American Statistical Association , 70 : 662 – 665 .
   Web of Science ®Google Scholar
• Stephens , M.A. 1974 . EDF statistics for goodness of fit and some comparisons . Journal of the American Statistical Association , 69 : 730 – 737 .
   Web of Science ®Google Scholar
• Swamy , P.A.V.B and Mehta , J.S. 1975 . Bayesian and non-Bayesian analysis of switching regressions and of random coefficient regression models . Journal of the American Statistical Association , 70 : 593 – 602 .
   Web of Science ®Google Scholar
• Theii and Henri . 1971 . Principles of econometrics , New York : John Wiley and sons .
   Google Scholar
• White , H. and MacDonald , G.M. 1980 . Some large-sample tests for nonnormality in the linear regression model . Journal of the American Statistical Association , 75 : 16 – 27 .
   Web of Science ®Google Scholar
• Wildt , A.R. 1977 . Estimating Models of Seasonal Market Response Using Dummy Variables . Journal of Marketing Research , 14 : 34 – 41 .
   Web of Science ®Google Scholar