Nonparametric Goodness-of-Fit Testing Under Gaussian Models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2003
|
| Schriftenreihe: | Lecture Notes in Statistics
169 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | In this book we would like to present some aspects of the modern theory of goodness-of-fit testing developed since the 1980s. Certainly we do not try to consider the subject in fuH. The selection of material corresponds to the author's interests. In particular, we consider the mathematical side of the problem; more exactly, we study asymptotically minimax setting in the problem (however, a lot of results are not asymptotic) and we do not discuss any related applied problems. To explain the main direction of our study we would like to make some general remarks. There are two main problems in Statistics: estimation theory and hypothesis testing. For the classical finite-parametric case these problems were studied in parallel. The studies are based on the common grounds: mainly they are Fisher's Maximum Likelihood principle and Le Cam's Local Asymptotical Normality property. On the other hand, a lot of statistical problems are not parametrie in the classical sense: the objects of estimation or testing are functions, im ages, and so on. These can be treated as unknown infinite-dimensional parameters which belong to specifie functional sets. This approach to non par ametrie estimation under asymptotically minimax setting was started in the 1960s-1970s by Chentsov, Ibragimov and Khasminskii, Pinsker, among others. It was developed very intensively for wide classes of functional sets and loss functions by Donoho, Johnstone, Kerkyacharian, Pieard, Lepski, Mammen, Spokoiny, Tsybakov, among others |
| Beschreibung: | 1 Online-Ressource (XIV, 457 p) |
| ISBN: | 9780387215808 9780387955315 |
| ISSN: | 0930-0325 |
| DOI: | 10.1007/978-0-387-21580-8 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV042418923 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150317s2003 |||| o||u| ||||||eng d | ||
| 020 | |a 9780387215808 |c Online |9 978-0-387-21580-8 | ||
| 020 | |a 9780387955315 |c Print |9 978-0-387-95531-5 | ||
| 024 | 7 | |a 10.1007/978-0-387-21580-8 |2 doi | |
| 035 | |a (OCoLC)1184495799 | ||
| 035 | |a (DE-599)BVBBV042418923 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-384 |a DE-703 |a DE-91 |a DE-634 | ||
| 082 | 0 | |a 519.5 |2 23 | |
| 084 | |a MAT 000 |2 stub | ||
| 100 | 1 | |a Ingster, Yu. I. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Nonparametric Goodness-of-Fit Testing Under Gaussian Models |c by Yu. I. Ingster, Irina A. Suslina |
| 264 | 1 | |a New York, NY |b Springer New York |c 2003 | |
| 300 | |a 1 Online-Ressource (XIV, 457 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Lecture Notes in Statistics |v 169 |x 0930-0325 | |
| 500 | |a In this book we would like to present some aspects of the modern theory of goodness-of-fit testing developed since the 1980s. Certainly we do not try to consider the subject in fuH. The selection of material corresponds to the author's interests. In particular, we consider the mathematical side of the problem; more exactly, we study asymptotically minimax setting in the problem (however, a lot of results are not asymptotic) and we do not discuss any related applied problems. To explain the main direction of our study we would like to make some general remarks. There are two main problems in Statistics: estimation theory and hypothesis testing. For the classical finite-parametric case these problems were studied in parallel. The studies are based on the common grounds: mainly they are Fisher's Maximum Likelihood principle and Le Cam's Local Asymptotical Normality property. On the other hand, a lot of statistical problems are not parametrie in the classical sense: the objects of estimation or testing are functions, im ages, and so on. These can be treated as unknown infinite-dimensional parameters which belong to specifie functional sets. This approach to non par ametrie estimation under asymptotically minimax setting was started in the 1960s-1970s by Chentsov, Ibragimov and Khasminskii, Pinsker, among others. It was developed very intensively for wide classes of functional sets and loss functions by Donoho, Johnstone, Kerkyacharian, Pieard, Lepski, Mammen, Spokoiny, Tsybakov, among others | ||
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 4 | |a Statistical Theory and Methods | |
| 650 | 4 | |a Statistik | |
| 650 | 0 | 7 | |a Nichtparametrischer Test |0 (DE-588)4075372-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Güte der Anpassung |0 (DE-588)4202143-1 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Güte der Anpassung |0 (DE-588)4202143-1 |D s |
| 689 | 0 | 1 | |a Nichtparametrischer Test |0 (DE-588)4075372-4 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Suslina, Irina A. |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-0-387-21580-8 |x Verlag |3 Volltext |
| 912 | |a ZDB-2-SMA |a ZDB-2-BAE | ||
| 940 | 1 | |q ZDB-2-SMA_Archive | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-027854340 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804153088980811776 |
|---|---|
| any_adam_object | |
| author | Ingster, Yu. I. |
| author_facet | Ingster, Yu. I. |
| author_role | aut |
| author_sort | Ingster, Yu. I. |
| author_variant | y i i yi yii |
| building | Verbundindex |
| bvnumber | BV042418923 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)1184495799 (DE-599)BVBBV042418923 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-0-387-21580-8 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03283nmm a2200493zcb4500</leader><controlfield tag="001">BV042418923</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150317s2003 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780387215808</subfield><subfield code="c">Online</subfield><subfield code="9">978-0-387-21580-8</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780387955315</subfield><subfield code="c">Print</subfield><subfield code="9">978-0-387-95531-5</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-0-387-21580-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1184495799</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042418923</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ingster, Yu. I.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nonparametric Goodness-of-Fit Testing Under Gaussian Models</subfield><subfield code="c">by Yu. I. Ingster, Irina A. Suslina</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Springer New York</subfield><subfield code="c">2003</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XIV, 457 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Lecture Notes in Statistics</subfield><subfield code="v">169</subfield><subfield code="x">0930-0325</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">In this book we would like to present some aspects of the modern theory of goodness-of-fit testing developed since the 1980s. Certainly we do not try to consider the subject in fuH. The selection of material corresponds to the author's interests. In particular, we consider the mathematical side of the problem; more exactly, we study asymptotically minimax setting in the problem (however, a lot of results are not asymptotic) and we do not discuss any related applied problems. To explain the main direction of our study we would like to make some general remarks. There are two main problems in Statistics: estimation theory and hypothesis testing. For the classical finite-parametric case these problems were studied in parallel. The studies are based on the common grounds: mainly they are Fisher's Maximum Likelihood principle and Le Cam's Local Asymptotical Normality property. On the other hand, a lot of statistical problems are not parametrie in the classical sense: the objects of estimation or testing are functions, im ages, and so on. These can be treated as unknown infinite-dimensional parameters which belong to specifie functional sets. This approach to non par ametrie estimation under asymptotically minimax setting was started in the 1960s-1970s by Chentsov, Ibragimov and Khasminskii, Pinsker, among others. It was developed very intensively for wide classes of functional sets and loss functions by Donoho, Johnstone, Kerkyacharian, Pieard, Lepski, Mammen, Spokoiny, Tsybakov, among others</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical Theory and Methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistik</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Nichtparametrischer Test</subfield><subfield code="0">(DE-588)4075372-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Güte der Anpassung</subfield><subfield code="0">(DE-588)4202143-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Güte der Anpassung</subfield><subfield code="0">(DE-588)4202143-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Nichtparametrischer Test</subfield><subfield code="0">(DE-588)4075372-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Suslina, Irina A.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-0-387-21580-8</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SMA</subfield><subfield code="a">ZDB-2-BAE</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-SMA_Archive</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027854340</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
| id | DE-604.BV042418923 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:03Z |
| institution | BVB |
| isbn | 9780387215808 9780387955315 |
| issn | 0930-0325 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027854340 |
| oclc_num | 1184495799 |
| open_access_boolean | |
| owner | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| owner_facet | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| physical | 1 Online-Ressource (XIV, 457 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | Springer New York |
| record_format | marc |
| series2 | Lecture Notes in Statistics |
| spelling | Ingster, Yu. I. Verfasser aut Nonparametric Goodness-of-Fit Testing Under Gaussian Models by Yu. I. Ingster, Irina A. Suslina New York, NY Springer New York 2003 1 Online-Ressource (XIV, 457 p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Statistics 169 0930-0325 In this book we would like to present some aspects of the modern theory of goodness-of-fit testing developed since the 1980s. Certainly we do not try to consider the subject in fuH. The selection of material corresponds to the author's interests. In particular, we consider the mathematical side of the problem; more exactly, we study asymptotically minimax setting in the problem (however, a lot of results are not asymptotic) and we do not discuss any related applied problems. To explain the main direction of our study we would like to make some general remarks. There are two main problems in Statistics: estimation theory and hypothesis testing. For the classical finite-parametric case these problems were studied in parallel. The studies are based on the common grounds: mainly they are Fisher's Maximum Likelihood principle and Le Cam's Local Asymptotical Normality property. On the other hand, a lot of statistical problems are not parametrie in the classical sense: the objects of estimation or testing are functions, im ages, and so on. These can be treated as unknown infinite-dimensional parameters which belong to specifie functional sets. This approach to non par ametrie estimation under asymptotically minimax setting was started in the 1960s-1970s by Chentsov, Ibragimov and Khasminskii, Pinsker, among others. It was developed very intensively for wide classes of functional sets and loss functions by Donoho, Johnstone, Kerkyacharian, Pieard, Lepski, Mammen, Spokoiny, Tsybakov, among others Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrischer Test (DE-588)4075372-4 gnd rswk-swf Güte der Anpassung (DE-588)4202143-1 gnd rswk-swf Güte der Anpassung (DE-588)4202143-1 s Nichtparametrischer Test (DE-588)4075372-4 s 1\p DE-604 Suslina, Irina A. Sonstige oth https://doi.org/10.1007/978-0-387-21580-8 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Ingster, Yu. I. Nonparametric Goodness-of-Fit Testing Under Gaussian Models Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrischer Test (DE-588)4075372-4 gnd Güte der Anpassung (DE-588)4202143-1 gnd |
| subject_GND | (DE-588)4075372-4 (DE-588)4202143-1 |
| title | Nonparametric Goodness-of-Fit Testing Under Gaussian Models |
| title_auth | Nonparametric Goodness-of-Fit Testing Under Gaussian Models |
| title_exact_search | Nonparametric Goodness-of-Fit Testing Under Gaussian Models |
| title_full | Nonparametric Goodness-of-Fit Testing Under Gaussian Models by Yu. I. Ingster, Irina A. Suslina |
| title_fullStr | Nonparametric Goodness-of-Fit Testing Under Gaussian Models by Yu. I. Ingster, Irina A. Suslina |
| title_full_unstemmed | Nonparametric Goodness-of-Fit Testing Under Gaussian Models by Yu. I. Ingster, Irina A. Suslina |
| title_short | Nonparametric Goodness-of-Fit Testing Under Gaussian Models |
| title_sort | nonparametric goodness of fit testing under gaussian models |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrischer Test (DE-588)4075372-4 gnd Güte der Anpassung (DE-588)4202143-1 gnd |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrischer Test Güte der Anpassung |
| url | https://doi.org/10.1007/978-0-387-21580-8 |
| work_keys_str_mv | AT ingsteryui nonparametricgoodnessoffittestingundergaussianmodels AT suslinairinaa nonparametricgoodnessoffittestingundergaussianmodels |