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Density Deconvolution with Replicate Measurements and Auxiliary Data
McIntyre, Julie P. - 2003
We present two deconvolution estimators for the density function of a random variable X that is measured with error … estimator generalizes the deconvolution estimator of Stefanski and Carroll (1990), with the measurement error variances … study and an example.The second is a semi-parametric deconvolution estimator that assumes the availability of a covariate …
Persistent link: https://www.econbiz.de/10009431272
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Ill-posed inverse problems in economics
Horowitz, Joel - 2013
A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter. Often, this mapping is obtained by inverting a mapping from the parameter to the population distribution. If the inverse...
Persistent link: https://www.econbiz.de/10010318682
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Ill-posed inverse problems in economics
Horowitz, Joel - Centre for Microdata Methods and Practice (CEMMAP) - 2013
A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter. Often, this mapping is obtained by inverting a mapping from the parameter to the population distribution. If the inverse...
Persistent link: https://www.econbiz.de/10010827506
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Ill-Posed Inverse Problems in Economics
Horowitz, Joel L. - In: Annual Review of Economics 6 (2014) 1, pp. 21-51
A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter. Often, this mapping is obtained by inverting a mapping from the parameter to the population distribution. If the inverse...
Persistent link: https://www.econbiz.de/10010886200
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A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian
Proenca, Isabel - EconWPA - 2005
Deconvolving kernel estimators when noise is Gaussian entail heavy calculations. In order to obtain the density estimates numerical evaluation of a specific integral is needed. This work proposes an approximation to the deconvolving kernel which simplifies considerably calculations by avoiding...
Persistent link: https://www.econbiz.de/10005062537
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Consistent Density Deconvolution under Partially Known Error Distribution
Schwarz, Maik; Van Bellegem, Sébastien - Institut d'Économie Industrielle (IDEI), Toulouse … - 2009
We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identifiable from the observations if we restrict the class of considered distributions by a...
Persistent link: https://www.econbiz.de/10008492575
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Consistent Density Deconvolution under Partially Known Error Distribution
Schwarz, Maik; Van Bellegem, Sébastien - Toulouse School of Economics (TSE) - 2009
We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identifiable from the observations if we restrict the class of considered distributions by a...
Persistent link: https://www.econbiz.de/10008465315
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Ill-posed inverse problems in economics
Horowitz, Joel - 2013
A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter. Often, this mapping is obtained by inverting a mapping from the parameter to the population distribution. If the inverse...
Persistent link: https://www.econbiz.de/10009778441
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Statistical inference for inverse problems
Bissantz, Nicolai; Holzmann, Hajo - 2007
deconvolution and the backward heat equation. Further, we discuss the construction of certain hypothesis tests, in particular …
Persistent link: https://www.econbiz.de/10010300678
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Well-posedness of measurement error models for self-reported data
An, Yonghong; Hu, Yingyao - 2009
It is widely admitted that the inverse problem of estimating the distribution of a latent variable X* from an observed sample of X, a contaminated measurement of X*, is ill-posed. This paper shows that measurement error models for self-reporting data are well-posed, assuming the probability of...
Persistent link: https://www.econbiz.de/10010277534