Applied Nonparametric Regression

By W. HÃ¤rdle. 1994

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “For this setting, the traditional methods in econometrics are based on kernel regression or nearest neighbor methods (Härdle [1990], Wasserman [2007]).”
Nonparametric specification testing for continuous-time models with application to spot interest rates (2002, unknown): “This phenomenon has been well documented in econometrics and statistics literature (e.g. see Härdle 1990).”
Financial satisfaction over the life course: The influence of assets and liabilities (2011, unknown): “7 See Härdle (1990) for an overview of nonparametric regressions.”
International Investment Positions and Exchange Rate Dynamics: A Dynamic Panel Analysis (2007, methodology): “ship To compute the MG, PMG, CPMG and SKMG estimators, we need to be assured that a long-run relation between the dependent variable, y, and the regressors, x, in the panel 22See, for example, Härdle (1990).”
Model-free model-fitting and predictive distributions (2010, background): “The reviews by Altman (1992) and Schucany (2004) give concise introductions to popular methods of nonparametric regression with emphasis on kernel smoothers; book-length treatments are given by Härdle (1990), Hart (1997), Fan and Gijbels (1996), and Loader (1999).”
Risk management based on stochastic volatility (2003, background): “Suppose that f ( · , ϑ ) is the density of ∆ L 1 , where ϑ denotes a possibly multivariate unknown parameter.”
Option-Implied Risk-Neutral Distributions and Risk Aversion (2004, unknown): “Aït-Sahalia and Lo (1998) Ker + Kernel estimator in stock price, strike, maturity, interest rate, and dividends Andersen and Wagener (2002) Cur +/– Higher-order splines fitted to implied volatilities Aparicio and Hodges (1998) Cur +/– Cubic B-splines fitted to implied volatilities Bondarenko (2000) ”
Inequality, Control Rights, and Rent Seeking: Sugar Cooperatives in Maharashtra (2001, methodology): “Pointwise confidence intervals are computed using a method suggested by Härdle (1990).”
Kernel Regression for Image Processing and Reconstruction (2007, background): “In the nonparametric statistics literature , locally constant, linear, and quadratic approximations (corresponding to 0, 1, 2) have been considered most widely [3], [12]–[14].”
All of Statistics: A Concise Course in Statistical Inference (2004, background): “Two good starting points are Hardle (1990) and Loader (1999). The latter emphasizes a class of techniques called local likelihood methods.”
Estimating a Convex Function in Nonparametric Regression (2007, background): “On the other hand, it is also important to discuss the impact of the choice of u0 in the case, where m is not convex, because in applications the preliminary estimate m̂ will rarely be convex (or its derivative m̂′ isotone) even if the true curve possesses this property.”
A Unifying Review of Deep and Shallow Anomaly Detection (2020, methodology): “This includes classic density estimation methods [246] as well as deep statistical models.”