Simple boundary correction for kernel density estimation

Jones, M. C.

doi:10.1007/bf00147776

Cited by 436 publications

(295 citation statements)

References 43 publications

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“…Density estimates computed using an Epanechnikov kernel. Since DWL is nonnegative in the constrained case, density is renormalized in the boundary area (Jones, 1993). Estimates computed using the same knot choice throughout as cross-validated for the median.…”

Section: Discussionmentioning

confidence: 99%

Nonparametric Estimation of a Nonseparable Demand Function under the Slutsky Inequality Restriction

Blundell

Horowitz²,

Parey

2017

The Review of Economics and Statistics

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Abstract-We present a method for consistent nonparametric estimation of a demand function with nonseparable unobserved taste heterogeneity subject to the shape restriction implied by the Slutsky inequality. We use the method to estimate gasoline demand in the United States. The results reveal differences in behavior between heavy and moderate gasoline users. They also reveal variation in the responsiveness of demand to plausible changes in prices across the income distribution. We extend our estimation method to permit endogeneity of prices. The empirical results illustrate the improvements in finite-sample performance of a nonparametric estimator from imposing shape restrictions based on economic theory.

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Section: Discussionmentioning

confidence: 99%

Nonparametric Estimation of a Nonseparable Demand Function under the Slutsky Inequality Restriction

Blundell

Horowitz²,

Parey

2017

The Review of Economics and Statistics

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“…As a benchmark we report the results for the unfeasible ISE optimal bandwidth, h ISE . All the numbers have been multiplied by 100. and right one-sided cross-validation using the local linear kernel density estimator (Jones, 1993;andCheng 1997a, 1997b). In fact the method cannot work on local constant density estimation because of its inferior rate of convergence when applying to asymmetric kernels.…”

Section: Designmentioning

confidence: 99%

Further theoretical and practical insight to the do-validated bandwidth selector

Mammen

Miranda

Nielsen

et al. 2014

Journal of the Korean Statistical Society

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This is the accepted version of the paper.This version of the publication may differ from the final published version. and that the recent do-validated method seems to provide the most practical alternative among these methods. In this paper we show step by step how classical cross-validation improves in theory, as well as in practice, from indirectness and that do-validated estimators improve in theory, but not in practice, from further indirectness. This paper therefore provides a strong support for the practical and theoretical properties of do-validated bandwidth selection. Do-validation is currently being introduced to survival analysis in a number of contexts and this paper provides evidence that this might be the immediate step forward. Permanent repository link

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“…We record how often this is done, as frequent boundary normalization is symptomatic of substantial boundary leakage. Alternative approaches that use special boundary kernels [Hall and Wehrly, 1991;Wand et al, 1991;Djojosugito and Speckman, 1992;Jones, 1993] or work with log-transformed data could be used in cases where this method for handling the boundaries is found to be unsatisfactory.…”

Section: Kernel Density Estimationmentioning

confidence: 99%

Disaggregation procedures for stochastic hydrology based on nonparametric density estimation

Tarboton¹,

Sharma²,

Lall³

1998

Water Resources Research

138

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Abstract. Synthetic simulation of streamflow sequences is important for the analysis of water supply reliability. Disaggregation models are an important component of the stochastic streamflow generation methodology. They provide the ability to simulate multiseason and multisite streamflow sequences that preserve statistical properties at multiple timescales or space scales. In recent papers we have suggested the use of nonparametric methods for streamflow simulation. These methods provide the capability to model time series dependence without a priori assumptions as to the probability distribution of streamflow. They remain faithful to the data and can approximate linear or nonlinear dependence. In this paper we extend the use of nonparametric methods to disaggregation models. We show how a kernel density estimate of the joint distribution of disaggregate flow variables can form the basis for conditional simulation based on an input aggregate flow variable. This methodology preserves summability of the disaggregate flows to the input aggregate flow. We show through applications to synthetic data and streamflow from the San Juan River in New Mexico how this conditional simulation procedure preserves a variety of statistical attributes. IntroductionA goal of stochastic hydrology is to generate synthetic streamflow sequences that are statistically similar to observed streamflow records. Such synthetic streamflow sequences are useful for analyzing reservoir operation and stream management policies. Often, multiple reservoir sites and stream sections need to be considered as part of a system operation plan, and the operating horizon may extend from a few days to several years. For proper system operation it is important that the streamflow sequences generated for the different sites and/or time periods be "compatible." Practically, this suggests that (1) the flow recorded at a downstream gage be represented as the sum of the tributary flows and channel losses/ gains, (2) the annual flow represent a sum of the monthly flows, (3) the monthly fractions of flows in wet/dry years be representative of wet/dry years respectively, and (4)

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Simple boundary correction for kernel density estimation

Cited by 436 publications

References 43 publications

Nonparametric Estimation of a Nonseparable Demand Function under the Slutsky Inequality Restriction

Nonparametric Estimation of a Nonseparable Demand Function under the Slutsky Inequality Restriction

Further theoretical and practical insight to the do-validated bandwidth selector

Disaggregation procedures for stochastic hydrology based on nonparametric density estimation

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