Smoothing Techniques: With Implementation in S

By Wolfgang Karl Härdle. 1991

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Citations in the corpus

Do Benefit Cuts Boost Job Findings? Swedish Evidence from the 1990s (2001, unknown): “For literature on nonparametric regressions, see Cleveland (1979), Cleveland, Devlin, and Grosse (1988), and Härdle (1990).”
Are bid preferences benign? The effect of small business subsidies in highway procurement auctions (2007, background): “Estimation of the components of (14) and (15) must be performed separately for preference and non-preference auctions and for each combination of the number of small and large bidders,”
Applications of Copulas to Analysis of Efficiency of Weather Derivatives as Primary Crop Insurance Instruments (2004, background): “where K(-) is a kernel function, and A is a degree of smoothness or bandwidth11 (Hardie, 1991).”
Application of Bernstein Polynomials for smooth estimation of a distribution and density function (2002, background): “The reader is referred to the excellent texts by H? ardle (1991) and Silverman (1986) for smoothing techniques for curve estimation.”
Mutual Information Analysis: a Comprehensive Study (2011, methodology): “In our case, we used the modified rule of thumb estimator [12, 27]:”
Semi-parametric modelling in finance: theoretical foundations (2002, background): “Härdle (1990). It turns out that the tail decay of g is an indicator of the likelihood of extreme events and thus the whole density generator g is informative about risk and risk-management questions assuming that risk is measured by the portfolio VaR (which is coherent in the sense of Artzner et al”
Bayesian density regression (2007, background): “The issues involved in choosing w(·) are related to those arising in choosing a kernel function in density estimation (Härdle, 1991), though the mechanics of how the function impacts the density estimator are very different.”
Conditionally independent private information in OCS wildcat auctions (2000, methodology): “Speci"cally, we use h G "2.978]1.06p( a (n¸)~1@5 and h g "2.978]1.06p( a (n¸)~1@6, where p( a is the standard deviation of the logarithm of (1#bids), and the factor 2.978 follows from the use of the triweight kernel instead of the Gaussian kernel (see Hardle, 1991).”
Asymmetry in first-price auctions with affiliated private values (2003, methodology): “The factor 2.978 follows from the use of the triweight kernel instead of the Gaussian kernel (see Hardle, 1991).”
Robot juggling: implementation of memory-based learning (1994, methodology): “Common algorithms to choose the subset or the weighting are, for example, n-nearest neighbor methods (e.g., [12, 25]) or kernel regression (e.g., [ 19 , 28, 37]).”