Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors
Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$...
Ausführliche Beschreibung
Autor*in: |
Phuong, Cao Xuan [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Übergeordnetes Werk: |
Enthalten in: Metrika - Berlin : Springer, 1958, 85(2021), 3 vom: 09. Juli, Seite 289-322 |
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Übergeordnetes Werk: |
volume:85 ; year:2021 ; number:3 ; day:09 ; month:07 ; pages:289-322 |
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DOI / URN: |
10.1007/s00184-021-00830-5 |
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Katalog-ID: |
SPR046370773 |
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520 | |a Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. | ||
650 | 4 | |a Cumulative distribution function |7 (dpeaa)DE-He213 | |
650 | 4 | |a Deconvolution |7 (dpeaa)DE-He213 | |
650 | 4 | |a Berkson errors |7 (dpeaa)DE-He213 | |
650 | 4 | |a Classical errors |7 (dpeaa)DE-He213 | |
700 | 1 | |a Thuy, Le Thi Hong |0 (orcid)0000-0003-1085-8505 |4 aut | |
700 | 1 | |a Doan, Vo Nguyen Tuyet |0 (orcid)0000-0002-2156-4708 |4 aut | |
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10.1007/s00184-021-00830-5 doi (DE-627)SPR046370773 (SPR)s00184-021-00830-5-e DE-627 ger DE-627 rakwb eng Phuong, Cao Xuan verfasserin (orcid)0000-0002-2943-1096 aut Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. Cumulative distribution function (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Berkson errors (dpeaa)DE-He213 Classical errors (dpeaa)DE-He213 Thuy, Le Thi Hong (orcid)0000-0003-1085-8505 aut Doan, Vo Nguyen Tuyet (orcid)0000-0002-2156-4708 aut Enthalten in Metrika Berlin : Springer, 1958 85(2021), 3 vom: 09. Juli, Seite 289-322 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:85 year:2021 number:3 day:09 month:07 pages:289-322 https://dx.doi.org/10.1007/s00184-021-00830-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 85 2021 3 09 07 289-322 |
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10.1007/s00184-021-00830-5 doi (DE-627)SPR046370773 (SPR)s00184-021-00830-5-e DE-627 ger DE-627 rakwb eng Phuong, Cao Xuan verfasserin (orcid)0000-0002-2943-1096 aut Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. Cumulative distribution function (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Berkson errors (dpeaa)DE-He213 Classical errors (dpeaa)DE-He213 Thuy, Le Thi Hong (orcid)0000-0003-1085-8505 aut Doan, Vo Nguyen Tuyet (orcid)0000-0002-2156-4708 aut Enthalten in Metrika Berlin : Springer, 1958 85(2021), 3 vom: 09. Juli, Seite 289-322 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:85 year:2021 number:3 day:09 month:07 pages:289-322 https://dx.doi.org/10.1007/s00184-021-00830-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 85 2021 3 09 07 289-322 |
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10.1007/s00184-021-00830-5 doi (DE-627)SPR046370773 (SPR)s00184-021-00830-5-e DE-627 ger DE-627 rakwb eng Phuong, Cao Xuan verfasserin (orcid)0000-0002-2943-1096 aut Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. Cumulative distribution function (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Berkson errors (dpeaa)DE-He213 Classical errors (dpeaa)DE-He213 Thuy, Le Thi Hong (orcid)0000-0003-1085-8505 aut Doan, Vo Nguyen Tuyet (orcid)0000-0002-2156-4708 aut Enthalten in Metrika Berlin : Springer, 1958 85(2021), 3 vom: 09. Juli, Seite 289-322 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:85 year:2021 number:3 day:09 month:07 pages:289-322 https://dx.doi.org/10.1007/s00184-021-00830-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 85 2021 3 09 07 289-322 |
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10.1007/s00184-021-00830-5 doi (DE-627)SPR046370773 (SPR)s00184-021-00830-5-e DE-627 ger DE-627 rakwb eng Phuong, Cao Xuan verfasserin (orcid)0000-0002-2943-1096 aut Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. Cumulative distribution function (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Berkson errors (dpeaa)DE-He213 Classical errors (dpeaa)DE-He213 Thuy, Le Thi Hong (orcid)0000-0003-1085-8505 aut Doan, Vo Nguyen Tuyet (orcid)0000-0002-2156-4708 aut Enthalten in Metrika Berlin : Springer, 1958 85(2021), 3 vom: 09. Juli, Seite 289-322 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:85 year:2021 number:3 day:09 month:07 pages:289-322 https://dx.doi.org/10.1007/s00184-021-00830-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 85 2021 3 09 07 289-322 |
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10.1007/s00184-021-00830-5 doi (DE-627)SPR046370773 (SPR)s00184-021-00830-5-e DE-627 ger DE-627 rakwb eng Phuong, Cao Xuan verfasserin (orcid)0000-0002-2943-1096 aut Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. Cumulative distribution function (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Berkson errors (dpeaa)DE-He213 Classical errors (dpeaa)DE-He213 Thuy, Le Thi Hong (orcid)0000-0003-1085-8505 aut Doan, Vo Nguyen Tuyet (orcid)0000-0002-2156-4708 aut Enthalten in Metrika Berlin : Springer, 1958 85(2021), 3 vom: 09. Juli, Seite 289-322 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:85 year:2021 number:3 day:09 month:07 pages:289-322 https://dx.doi.org/10.1007/s00184-021-00830-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 85 2021 3 09 07 289-322 |
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Enthalten in Metrika 85(2021), 3 vom: 09. Juli, Seite 289-322 volume:85 year:2021 number:3 day:09 month:07 pages:289-322 |
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Phuong, Cao Xuan @@aut@@ Thuy, Le Thi Hong @@aut@@ Doan, Vo Nguyen Tuyet @@aut@@ |
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Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cumulative distribution function</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deconvolution</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Berkson errors</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classical errors</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Thuy, Le Thi Hong</subfield><subfield code="0">(orcid)0000-0003-1085-8505</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Doan, Vo Nguyen Tuyet</subfield><subfield code="0">(orcid)0000-0002-2156-4708</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Metrika</subfield><subfield code="d">Berlin : Springer, 1958</subfield><subfield code="g">85(2021), 3 vom: 09. 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Phuong, Cao Xuan |
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Phuong, Cao Xuan misc Cumulative distribution function misc Deconvolution misc Berkson errors misc Classical errors Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors |
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Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors Cumulative distribution function (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Berkson errors (dpeaa)DE-He213 Classical errors (dpeaa)DE-He213 |
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nonparametric estimation of cumulative distribution function from noisy data in the presence of berkson and classical errors |
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Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors |
abstract |
Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
abstractGer |
Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
abstract_unstemmed |
Abstract Let X, Y, W, %$\delta %$ and %$\varepsilon %$ be continuous univariate random variables defined on a probability space such that %$Y = X+\varepsilon %$ and %$W = X + \delta %$. Herein X, %$\delta %$ and %$\varepsilon %$ are assumed to be mutually independent. The variables %$\varepsilon %$ and %$\delta %$ are called classical and Berkson errors, respectively. Their distributions are known exactly. Suppose we only observe a random sample %$Y_1, \ldots , Y_n%$ from the distribution of Y. This paper is devoted to a nonparametric estimation of the unknown cumulative distribution function %$F_W%$ of W based on the observations as well as on the distributions of %$\varepsilon %$, %$\delta %$. An estimator for %$F_W%$ depending on a smoothing parameter is suggested. It is shown to be consistent with respect to the mean squared error. Under certain regularity assumptions on the densities of X, %$\delta %$ and %$\varepsilon %$, we establish some upper and lower bounds on the convergence rate of the proposed estimator. Finally, we perform some numerical examples to illustrate our theoretical results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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3 |
title_short |
Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors |
url |
https://dx.doi.org/10.1007/s00184-021-00830-5 |
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author2 |
Thuy, Le Thi Hong Doan, Vo Nguyen Tuyet |
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Thuy, Le Thi Hong Doan, Vo Nguyen Tuyet |
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doi_str |
10.1007/s00184-021-00830-5 |
up_date |
2024-07-03T22:07:44.126Z |
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score |
7.4014053 |