Parametric and Nonparametric Inference from Record-Breaking Data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2003
|
| Schriftenreihe: | Lecture Notes in Statistics
172 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | As statisticians, we are constantly trying to make inferences about the underlying population from which data are observed. This includes estimation and prediction about the underlying population parameters from both complete and incomplete data. Recently, methodology for estimation and prediction from incomplete data has been found useful for what is known as "record-breaking data," that is, data generated from setting new records. There has long been a keen interest in observing all kinds of records-in particular, sports records, financial records, flood records, and daily temperature records, to mention a few. The well-known Guinness Book of World Records is full of this kind of record information. As usual, beyond the general interest in knowing the last or current record value, the statistical problem of prediction of the next record based on past records has also been an important area of record research. Probabilistic and statistical models to describe behavior and make predictions from record-breaking data have been developed only within the last fifty or so years, with a relatively large amount of literature appearing on the subject in the last couple of decades. This book, written from a statistician's perspective, is not a compilation of "records," rather, it deals with the statistical issues of inference from a type of incomplete data, record-breaking data, observed as successive record values (maxima or minima) arising from a phenomenon or situation under study. Prediction is just one aspect of statistical inference based on observed record values |
| Beschreibung: | 1 Online-Ressource (VIII, 117 p) |
| ISBN: | 9780387215495 9780387001388 |
| ISSN: | 0930-0325 |
| DOI: | 10.1007/978-0-387-21549-5 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV042418910 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150317s2003 |||| o||u| ||||||eng d | ||
| 020 | |a 9780387215495 |c Online |9 978-0-387-21549-5 | ||
| 020 | |a 9780387001388 |c Print |9 978-0-387-00138-8 | ||
| 024 | 7 | |a 10.1007/978-0-387-21549-5 |2 doi | |
| 035 | |a (OCoLC)1184489421 | ||
| 035 | |a (DE-599)BVBBV042418910 | ||
| 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 Gulati, Sneh |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Parametric and Nonparametric Inference from Record-Breaking Data |c by Sneh Gulati, William J. Padgett |
| 264 | 1 | |a New York, NY |b Springer New York |c 2003 | |
| 300 | |a 1 Online-Ressource (VIII, 117 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Lecture Notes in Statistics |v 172 |x 0930-0325 | |
| 500 | |a As statisticians, we are constantly trying to make inferences about the underlying population from which data are observed. This includes estimation and prediction about the underlying population parameters from both complete and incomplete data. Recently, methodology for estimation and prediction from incomplete data has been found useful for what is known as "record-breaking data," that is, data generated from setting new records. There has long been a keen interest in observing all kinds of records-in particular, sports records, financial records, flood records, and daily temperature records, to mention a few. The well-known Guinness Book of World Records is full of this kind of record information. As usual, beyond the general interest in knowing the last or current record value, the statistical problem of prediction of the next record based on past records has also been an important area of record research. Probabilistic and statistical models to describe behavior and make predictions from record-breaking data have been developed only within the last fifty or so years, with a relatively large amount of literature appearing on the subject in the last couple of decades. This book, written from a statistician's perspective, is not a compilation of "records," rather, it deals with the statistical issues of inference from a type of incomplete data, record-breaking data, observed as successive record values (maxima or minima) arising from a phenomenon or situation under study. Prediction is just one aspect of statistical inference based on observed record values | ||
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 4 | |a Statistical Theory and Methods | |
| 650 | 4 | |a Statistik | |
| 650 | 0 | 7 | |a Nichtparametrisches Verfahren |0 (DE-588)4339273-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistische Schlussweise |0 (DE-588)4182963-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Parametrisches Verfahren |0 (DE-588)4205938-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Statistische Schlussweise |0 (DE-588)4182963-3 |D s |
| 689 | 0 | 1 | |a Parametrisches Verfahren |0 (DE-588)4205938-0 |D s |
| 689 | 0 | 2 | |a Nichtparametrisches Verfahren |0 (DE-588)4339273-8 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Padgett, William J. |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-0-387-21549-5 |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:BVB01-027854327 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804153088918945792 |
|---|---|
| any_adam_object | |
| author | Gulati, Sneh |
| author_facet | Gulati, Sneh |
| author_role | aut |
| author_sort | Gulati, Sneh |
| author_variant | s g sg |
| building | Verbundindex |
| bvnumber | BV042418910 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)1184489421 (DE-599)BVBBV042418910 |
| 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 | 10.1007/978-0-387-21549-5 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03517nmm a2200517zcb4500</leader><controlfield tag="001">BV042418910</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">9780387215495</subfield><subfield code="c">Online</subfield><subfield code="9">978-0-387-21549-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780387001388</subfield><subfield code="c">Print</subfield><subfield code="9">978-0-387-00138-8</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-0-387-21549-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1184489421</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042418910</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">Gulati, Sneh</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parametric and Nonparametric Inference from Record-Breaking Data</subfield><subfield code="c">by Sneh Gulati, William J. Padgett</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 (VIII, 117 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">172</subfield><subfield code="x">0930-0325</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">As statisticians, we are constantly trying to make inferences about the underlying population from which data are observed. This includes estimation and prediction about the underlying population parameters from both complete and incomplete data. Recently, methodology for estimation and prediction from incomplete data has been found useful for what is known as "record-breaking data," that is, data generated from setting new records. There has long been a keen interest in observing all kinds of records-in particular, sports records, financial records, flood records, and daily temperature records, to mention a few. The well-known Guinness Book of World Records is full of this kind of record information. As usual, beyond the general interest in knowing the last or current record value, the statistical problem of prediction of the next record based on past records has also been an important area of record research. Probabilistic and statistical models to describe behavior and make predictions from record-breaking data have been developed only within the last fifty or so years, with a relatively large amount of literature appearing on the subject in the last couple of decades. This book, written from a statistician's perspective, is not a compilation of "records," rather, it deals with the statistical issues of inference from a type of incomplete data, record-breaking data, observed as successive record values (maxima or minima) arising from a phenomenon or situation under study. Prediction is just one aspect of statistical inference based on observed record values</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">Nichtparametrisches Verfahren</subfield><subfield code="0">(DE-588)4339273-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Statistische Schlussweise</subfield><subfield code="0">(DE-588)4182963-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Parametrisches Verfahren</subfield><subfield code="0">(DE-588)4205938-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Statistische Schlussweise</subfield><subfield code="0">(DE-588)4182963-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Parametrisches Verfahren</subfield><subfield code="0">(DE-588)4205938-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Nichtparametrisches Verfahren</subfield><subfield code="0">(DE-588)4339273-8</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">Padgett, William J.</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/10.1007/978-0-387-21549-5</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:BVB01-027854327</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.BV042418910 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:03Z |
| institution | BVB |
| isbn | 9780387215495 9780387001388 |
| issn | 0930-0325 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027854327 |
| oclc_num | 1184489421 |
| 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 (VIII, 117 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 | Gulati, Sneh Verfasser aut Parametric and Nonparametric Inference from Record-Breaking Data by Sneh Gulati, William J. Padgett New York, NY Springer New York 2003 1 Online-Ressource (VIII, 117 p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Statistics 172 0930-0325 As statisticians, we are constantly trying to make inferences about the underlying population from which data are observed. This includes estimation and prediction about the underlying population parameters from both complete and incomplete data. Recently, methodology for estimation and prediction from incomplete data has been found useful for what is known as "record-breaking data," that is, data generated from setting new records. There has long been a keen interest in observing all kinds of records-in particular, sports records, financial records, flood records, and daily temperature records, to mention a few. The well-known Guinness Book of World Records is full of this kind of record information. As usual, beyond the general interest in knowing the last or current record value, the statistical problem of prediction of the next record based on past records has also been an important area of record research. Probabilistic and statistical models to describe behavior and make predictions from record-breaking data have been developed only within the last fifty or so years, with a relatively large amount of literature appearing on the subject in the last couple of decades. This book, written from a statistician's perspective, is not a compilation of "records," rather, it deals with the statistical issues of inference from a type of incomplete data, record-breaking data, observed as successive record values (maxima or minima) arising from a phenomenon or situation under study. Prediction is just one aspect of statistical inference based on observed record values Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 gnd rswk-swf Parametrisches Verfahren (DE-588)4205938-0 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 s Parametrisches Verfahren (DE-588)4205938-0 s Nichtparametrisches Verfahren (DE-588)4339273-8 s 1\p DE-604 Padgett, William J. Sonstige oth https://doi.org/10.1007/978-0-387-21549-5 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Gulati, Sneh Parametric and Nonparametric Inference from Record-Breaking Data Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Parametrisches Verfahren (DE-588)4205938-0 gnd |
| subject_GND | (DE-588)4339273-8 (DE-588)4182963-3 (DE-588)4205938-0 |
| title | Parametric and Nonparametric Inference from Record-Breaking Data |
| title_auth | Parametric and Nonparametric Inference from Record-Breaking Data |
| title_exact_search | Parametric and Nonparametric Inference from Record-Breaking Data |
| title_full | Parametric and Nonparametric Inference from Record-Breaking Data by Sneh Gulati, William J. Padgett |
| title_fullStr | Parametric and Nonparametric Inference from Record-Breaking Data by Sneh Gulati, William J. Padgett |
| title_full_unstemmed | Parametric and Nonparametric Inference from Record-Breaking Data by Sneh Gulati, William J. Padgett |
| title_short | Parametric and Nonparametric Inference from Record-Breaking Data |
| title_sort | parametric and nonparametric inference from record breaking data |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Parametrisches Verfahren (DE-588)4205938-0 gnd |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrisches Verfahren Statistische Schlussweise Parametrisches Verfahren |
| url | https://doi.org/10.1007/978-0-387-21549-5 |
| work_keys_str_mv | AT gulatisneh parametricandnonparametricinferencefromrecordbreakingdata AT padgettwilliamj parametricandnonparametricinferencefromrecordbreakingdata |