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Goodness-of-fit tests based on the kernel density estimator (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Cao, Ricardo |
| Abstract | ABSTRACT. Given an i.i.d. sample drawn from a density f on the real line, the problem of testing whether f is in a given class of densities is considered. Testing procedures constructed on the basis of minimizing the L1-distance between a kernel density estimate and any density in the hypothesized class are investigated. General non-asymptotic bounds are derived for the power of the test. It is shown that the concentration of the data-dependent smoothing factor and the ‘size ’ of the hypothesized class of densities play a key role in the performance of the test. Consistency and nonasymptotic performance bounds are established in several special cases, including testing simple hypotheses, translation/scale classes and symmetry. Simulations are also carried out to compare the behaviour of the method with the Kolmogorov-Smirnov test and an L 2 density-based approach due to Fan [Econ. Theory 10 (1994) 316]. Key words: bandwidth, goodness-of-fit test, kernel density estimator, smoothing factor selection |
| File Format | |
| Volume Number | 32 |
| Journal | Scandinavian Journal of Statistics |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Goodness-of-fit Test Kernel Density Estimator Hypothesized Class Sample Drawn Real Line Density-based Approach Several Special Case Density Estimator Data-dependent Smoothing Factor Fan Econ Key Word General Non-asymptotic Bound Kolmogorov-smirnov Test Translation Scale Class Nonasymptotic Performance Bound Key Role Simple Hypothesis Factor Selection Kernel Density Estimate |
| Content Type | Text |
| Resource Type | Article |
