Conditional density estimation in measurement error problems
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DOI: 10.1016/j.jmva.2014.08.011
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- Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
- Jianqing Fan & Tsz Ho Yim, 2004. "A crossvalidation method for estimating conditional densities," Biometrika, Biometrika Trust, vol. 91(4), pages 819-834, December.
- Hall, Peter & Wand, Matthew P., 1988. "On the minimization of absolute distance in kernel density estimation," Statistics & Probability Letters, Elsevier, vol. 6(5), pages 311-314, April.
- Wang, Xiao-Feng & Wang, Bin, 2011. "Deconvolution Estimation in Measurement Error Models: The R Package decon," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i10).
- Hall, Peter & Wand, Matthew P., 1988. "Minimizing L1 distance in nonparametric density estimation," Journal of Multivariate Analysis, Elsevier, vol. 26(1), pages 59-88, July.
- Peter Hall & Tapabrata Maiti, 2009. "Deconvolution methods for non‐parametric inference in two‐level mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 703-718, June.
- Bashtannyk, David M. & Hyndman, Rob J., 2001.
"Bandwidth selection for kernel conditional density estimation,"
Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 279-298, May.
- Bashtannyk, D.M. & Hyndman, R.J., 1998. "Bandwidth Selection for Kernel Conditional Density Estimation," Monash Econometrics and Business Statistics Working Papers 16/98, Monash University, Department of Econometrics and Business Statistics.
- Delaigle, A. & Gijbels, I., 2004. "Practical bandwidth selection in deconvolution kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 249-267, March.
- Wang, Xiao-Feng & Fan, Zhaozhi & Wang, Bin, 2010. "Estimating smooth distribution function in the presence of heteroscedastic measurement errors," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 25-36, January.
- Julie McIntyre & Leonard Stefanski, 2011. "Density Estimation with Replicate Heteroscedastic Measurements," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(1), pages 81-99, February.
- Jan G. De Gooijer & Dawit Zerom, 2003.
"On Conditional Density Estimation,"
Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(2), pages 159-176, May.
- Jan G. de Gooijer & Dawit Zerom, 2002. "On Conditional Density Estimation," Tinbergen Institute Discussion Papers 02-032/4, Tinbergen Institute.
- Xiao-Feng Wang & Deping Ye, 2012. "The effects of error magnitude and bandwidth selection for deconvolution with unknown error distribution," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(1), pages 153-167.
- F. Comte & C. Lacour, 2011. "Data‐driven density estimation in the presence of additive noise with unknown distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 601-627, September.
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Keywords
Measurement error; Gene microarray; Conditional density; Deconvolution; Ridge parameter; Kernel; Bandwidth selection;All these keywords.
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