Testing goodness of fit via nonparametric function estimation techniques

Eubank, R. L.; Hart, Jeffrey D.; LaRiccia, V. N.

doi:10.1080/03610929308831219

Cited by 38 publications

(16 citation statements)

References 25 publications

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“…Therefore, a good procedure for choosing k is very welcome. Some data driven versions of smooth tests have been recently proposed by Bickel and Ritov (1992), Eubank and LaRiccia (1992), Eubank, Hart and LaRiccia (1993), Ledwina (1994), Kallenberg and Ledwina (1995a) and Fan (1996). Extensive simulations presented in Ledwina (1994) and Kallenberg and Ledwina (1995a) show that the data driven Neyman's test proposed in Ledwina (1994) and extended in Kallenberg and Ledwina (1995a) compares very well to classical tests and other competitors.…”

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confidence: 89%

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Data driven smooth tests for composite hypotheses

Inglot¹,

Kallenberg²,

Ledwina³

1997

Ann. Statist.

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The classical problem of testing goodness-of-fit of a parametric family is reconsidered. A new test for this problem is proposed and investigated. The new test statistic is a combination of the smooth test statistic and Schwarz's selection rule. More precisely, as the sample size increases, an increasing family of exponential models describing departures from the null model is introduced and Schwarz's selection rule is presented to select among them. Schwarz's rule provides the "right" dimension given by the data, while the smooth test in the "right" dimension finishes the job. Theoretical properties of the selection rules are derived under null and alternative hypotheses. They imply consistency of data driven smooth tests for composite hypotheses at essentially any alternative.

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confidence: 89%

“…Eubank, Hart and LaRiccia (1993) and references mentioned there] include dimension 0 as a candidate dimension or even take the selection rule itself as test statistic, others [e.g., Bickel and Ritov (1992)] start from dimension 1. We prefer the latter approach as argued in Kallenberg and Ledwina (1995a), Section 2.…”

mentioning

confidence: 99%

Data driven smooth tests for composite hypotheses

Inglot¹,

Kallenberg²,

Ledwina³

1997

Ann. Statist.

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“…Zheng Eubank et al (1987), Ghosh and Huang (1991), Eubank and LaRiccia (1992), Eubank et al (1993), Zheng (2000), Zheng (2008), Bickel et al (2006), and others (see Hart 1997 for some surveys on the related work). The work in the literature has focused on testing for unconditional distributions of continuous variables.…”

Section: Introductionmentioning

confidence: 99%

Testing parametric conditional distributions using the nonparametric smoothing method

2010

Metrika

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This paper proposes a new goodness-of-fit test for parametric conditional probability distributions using the nonparametric smoothing methodology. An asymptotic normal distribution is established for the test statistic under the null hypothesis of correct specification of the parametric distribution. The test is shown to have power against local alternatives converging to the null at certain rates. The test can be applied to testing for possible misspecifications in a wide variety of parametric models. A bootstrap procedure is provided for obtaining more accurate critical values for the test. Monte Carlo simulations show that the test has good power against some common alternatives.

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“…case and for the so‐called sharp‐peak or high frequency alternatives, goodness‐of‐fit tests based on smoothed density estimators are more powerful than tests based on the cumulative distribution function; e.g. Rosenblatt (1975), Ghosh and Huang (1991), Eubank and LaRiccia (1992) and Eubank et al . (1993).…”

Section: Introductionmentioning

confidence: 99%

Testing the Fit of a Vector Autoregressive Moving Average Model

Paparoditis

2005

Journal Time Series Analysis

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A new procedure for testing the fit of multivariate time series model is proposed. The method evaluates in a certain way the closeness of the sample spectral density matrix of the observed process to the spectral density matrix of the parametric model postulated under the null and uses for this purpose nonparametric estimation techniques. The asymptotic distribution of the test statistic is established and an alternative, bootstrap-based method is developed in order to estimate more accurately this distribution under the null hypothesis. Goodness-of-fit diagnostics useful in understanding the test results and identifying sources of model inadequacy are introduced. The applicability of the testing procedure and its capability to detect lacks of fit is demonstrated by means of some real data examples.

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Testing goodness of fit via nonparametric function estimation techniques

Cited by 38 publications

References 25 publications

Data driven smooth tests for composite hypotheses

Data driven smooth tests for composite hypotheses

Testing parametric conditional distributions using the nonparametric smoothing method

Testing the Fit of a Vector Autoregressive Moving Average Model

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