Comparison of Regression Curves Using Quasi-Residuals

Abstract We consider testing the equality of two regression functions using two independent samples. Three tests are proposed that are free of the restriction of having the same covariate values or sample sizes for both samples. Asymptotic distributions are given and results from a simulation study are presented that show the superior power properties of these tests over a competing test in a variety of cases, including the testing of hypotheses involving high-frequency curves when the design points for the two samples differ. It is also observed that the tests have good level properties when proper smoothing parameters are selected.

[1]  Peter Hall,et al.  Bootstrap test for difference between means in nonparametric regression , 1990 .

[2]  J. Durbin Distribution theory for tests based on the sample distribution function , 1973 .

[3]  Vincent N. LaRiccia,et al.  Testing goodness of fit via nonparametric function estimation techniques , 1993 .

[4]  Jeffrey D. Hart,et al.  Testing the equality of two regression curves using linear smoothers , 1991 .

[5]  A. Shiryaev,et al.  Limit Theorems for Stochastic Processes , 1987 .

[6]  Clifford H. Spiegelman,et al.  Testing the Goodness of Fit of a Linear Model via Nonparametric Regression Techniques , 1990 .

[7]  P. Munson,et al.  A cubic spline extension of the Durbin-Watson test , 1989 .

[8]  Grace Wahba,et al.  Testing the (Parametric) Null Model Hypothesis in (Semiparametric) Partial and Generalized Spline Models , 1988 .

[9]  V. LaRiccia,et al.  Asymptotic Comparison of Cramer-von Mises and Nonparametric Function Estimation Techniques for Testing Goodness-of-Fit , 1992 .

[10]  H. Müller Nonparametric regression analysis of longitudinal data , 1988 .

[11]  James Stephen Marron,et al.  Semiparametric Comparison of Regression Curves , 1990 .

[12]  P. Billingsley,et al.  Convergence of Probability Measures , 1969 .

[13]  R. L. Eubank,et al.  Commonality of cusum, von Neumann and smoothing-based goodness-of-fit tests , 1993 .

[14]  William S. Cleveland,et al.  Visualizing Data , 1993 .

[15]  Ruth J. Williams,et al.  Introduction to Stochastic Integration , 1994 .

[16]  B. K. Ghosh,et al.  The Power and Optimal Kernel of the Bickel-Rosenblatt Test for Goodness of Fit , 1991 .

[17]  M. C. Jones,et al.  Spline Smoothing and Nonparametric Regression. , 1989 .

[18]  J. Rice Bandwidth Choice for Nonparametric Regression , 1984 .

[19]  R. L. Eubank,et al.  Testing Goodness-of-Fit in Regression Via Order Selection Criteria , 1992 .

[20]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[21]  P. Hall,et al.  Asymptotically optimal difference-based estimation of variance in nonparametric regression , 1990 .

[22]  Jianqing Fan Design-adaptive Nonparametric Regression , 1992 .