An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative

By Joel L. Horowitz, Vladimir Spokoiny. 2001

375 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/84efabce518a777c404b8fbe5acd32060791428f

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Citations in the corpus

ADAPTIVE TESTS OF LINEAR HYPOTHESES BY MODEL SELECTION (2003, methodology): “The test proposed by Horowitz and Spokoiny (2001).”
Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model (2009, methodology): “We choose the Gaussian kernel: k(u) = exp(−u2/ 2)/ √ 2π and select the bandwidth following the lead of Horowitz and Spokoiny (2001) and Su and Ullah (2009).”
A new statistic and practical guidelines for nonparametric Granger causality testing (2006, methodology): “For example, as in Horowitz and Spokoiny (2001) one might consider a new test statistic which is the largest value of the standardized and studentized test statistics for a range of bandwidths, and use bootstrap methods to assess the critical values.”
Nonparametric Identification and Estimation of a Censored Location-Scale Regression Model (2005, methodology): “The estimator introduced here suggest testing parametric forms of the regression function against nonparametric alternatives in the censored regression model, as has been done in standard regression models (Bierens and Ploberger 1997; Horowitz and Spokoiny 2001).”
Testing Parametric Conditional Distributions of Dynamic Models (2003, methodology): “It should be pointed out, however, n-consistent tests are not necessarily more powerful than those that are not n-consistent but can adapt to unknown smoothness of the alternatives, as showed by Horowitz and Spokoiny (2001). As an application of lemma 2, consider the local power of the test for GARC”
Breaking the curse of dimensionality in nonparametric testing (2008, background): “When the smoothness index s is unknown, the so-called adaptive rate is less by a small factor, see Spokoiny (1996), Horowitz and Spokoiny (2001), and Guerre and Lavergne (2005).”
A consistent model specification test with mixed discrete and continuous data (2007, methodology): “Recently, Horowitz and Spokoiny (2001) propose using a range of smoothing parameters in a kernel-based test to guard against low power in the direction of either low or high frequency alternatives.”
An empirical likelihood goodness‐of‐fit test for time series (2003, background): “Recently Horowitz and Spokoiny (2001) proposed a test for independent data that applies the Härdle–Mammen test simultaneously on a set of bandwidth values.”
Data-Driven Rate-Optimal Specification Testing in Regression Models (2005, methodology): “Finally, for the sake of parsimony, and following Horowitz and Spokoiny (2001), Lepski, Mammen and Spokoiny (1997) and Spokoiny (2001), the set Hn of admissible smoothing parameters is a geometric grid of Jn + 1 smoothing parameters, Hn = {hj = h0a, j = 0, .”
A difference based approach to the semiparametric partial linear model (2011, neutral): “[21] used differencing for testing between a parametric model and a nonparametric alternative.”
TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS (2012, background): “See also Lehmann and Romano (2005) for a textbook introduction.”
TESTING FOR WHITE NOISE UNDER UNKNOWN DEPENDENCE AND ITS APPLICATIONS TO DIAGNOSTIC CHECKING FOR TIME SERIES MODELS (2010, background): “The latter problem has been well studied in the literature, and the data-driven bandwidth choice for the smoothing type test has been addressed in Horowitz and Spokoiny (2001) and Guerre and Lavergne (2005), among others.”
Bandwidth Selection in Nonparametric Kernel Testing (2008, methodology): “The other parts of the table are obtained and tabulated similarly to table 1 of Horowitz and Spokoiny (2001).”