Model Selection and Inference: A Practical Information-Theoretic Approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1998
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | We wrote this book to introduce graduate students and research workers in var ious scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. In its fully developed form, the information-theoretic approach allows inference based on more than one model (including estimates of unconditional precision); in its initial form, it is useful in selecting a "best" model and ranking the remaining models. We believe that often the critical issue in data analysis is the selection of a good approximating model that best represents the inference supported by the data (an estimated "best approximating model"). In formation theory includes the well-known Kullback-Leibler "distance" between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) Kullback-Leibler distance based on Fisher's maximized log-likelihood. His mea sure, now called Akaike 's information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a funda mental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. We do not accept the notion that there is a simple, "true model" in the biological sciences |
| Beschreibung: | 1 Online-Ressource (XX, 355 p) |
| ISBN: | 9781475729177 9781475729191 |
| DOI: | 10.1007/978-1-4757-2917-7 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV042421398 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150317s1998 |||| o||u| ||||||eng d | ||
| 020 | |a 9781475729177 |c Online |9 978-1-4757-2917-7 | ||
| 020 | |a 9781475729191 |c Print |9 978-1-4757-2919-1 | ||
| 024 | 7 | |a 10.1007/978-1-4757-2917-7 |2 doi | |
| 035 | |a (OCoLC)863936778 | ||
| 035 | |a (DE-599)BVBBV042421398 | ||
| 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 Burnham, Kenneth P. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Model Selection and Inference |b A Practical Information-Theoretic Approach |c by Kenneth P. Burnham, David R. Anderson |
| 264 | 1 | |a New York, NY |b Springer New York |c 1998 | |
| 300 | |a 1 Online-Ressource (XX, 355 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a We wrote this book to introduce graduate students and research workers in var ious scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. In its fully developed form, the information-theoretic approach allows inference based on more than one model (including estimates of unconditional precision); in its initial form, it is useful in selecting a "best" model and ranking the remaining models. We believe that often the critical issue in data analysis is the selection of a good approximating model that best represents the inference supported by the data (an estimated "best approximating model"). In formation theory includes the well-known Kullback-Leibler "distance" between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) Kullback-Leibler distance based on Fisher's maximized log-likelihood. His mea sure, now called Akaike 's information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a funda mental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. We do not accept the notion that there is a simple, "true model" in the biological sciences | ||
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 4 | |a Statistical Theory and Methods | |
| 650 | 4 | |a Statistik | |
| 650 | 0 | 7 | |a Mathematisches Modell |0 (DE-588)4114528-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Biologie |0 (DE-588)4006851-1 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Biologie |0 (DE-588)4006851-1 |D s |
| 689 | 0 | 1 | |a Mathematisches Modell |0 (DE-588)4114528-8 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Anderson, David R. |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4757-2917-7 |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:BVB01-027856815 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804153094407192576 |
|---|---|
| any_adam_object | |
| author | Burnham, Kenneth P. |
| author_facet | Burnham, Kenneth P. |
| author_role | aut |
| author_sort | Burnham, Kenneth P. |
| author_variant | k p b kp kpb |
| building | Verbundindex |
| bvnumber | BV042421398 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)863936778 (DE-599)BVBBV042421398 |
| 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 | 10.1007/978-1-4757-2917-7 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03136nmm a2200481zc 4500</leader><controlfield tag="001">BV042421398</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150317s1998 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781475729177</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-4757-2917-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781475729191</subfield><subfield code="c">Print</subfield><subfield code="9">978-1-4757-2919-1</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4757-2917-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)863936778</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042421398</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">Burnham, Kenneth P.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Model Selection and Inference</subfield><subfield code="b">A Practical Information-Theoretic Approach</subfield><subfield code="c">by Kenneth P. Burnham, David R. Anderson</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">1998</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XX, 355 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="500" ind1=" " ind2=" "><subfield code="a">We wrote this book to introduce graduate students and research workers in var ious scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. In its fully developed form, the information-theoretic approach allows inference based on more than one model (including estimates of unconditional precision); in its initial form, it is useful in selecting a "best" model and ranking the remaining models. We believe that often the critical issue in data analysis is the selection of a good approximating model that best represents the inference supported by the data (an estimated "best approximating model"). In formation theory includes the well-known Kullback-Leibler "distance" between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) Kullback-Leibler distance based on Fisher's maximized log-likelihood. His mea sure, now called Akaike 's information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a funda mental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. We do not accept the notion that there is a simple, "true model" in the biological sciences</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">Mathematisches Modell</subfield><subfield code="0">(DE-588)4114528-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Biologie</subfield><subfield code="0">(DE-588)4006851-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Biologie</subfield><subfield code="0">(DE-588)4006851-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Mathematisches Modell</subfield><subfield code="0">(DE-588)4114528-8</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">Anderson, David R.</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/10.1007/978-1-4757-2917-7</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:BVB01-027856815</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.BV042421398 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:08Z |
| institution | BVB |
| isbn | 9781475729177 9781475729191 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027856815 |
| oclc_num | 863936778 |
| 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 (XX, 355 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1998 |
| publishDateSearch | 1998 |
| publishDateSort | 1998 |
| publisher | Springer New York |
| record_format | marc |
| spelling | Burnham, Kenneth P. Verfasser aut Model Selection and Inference A Practical Information-Theoretic Approach by Kenneth P. Burnham, David R. Anderson New York, NY Springer New York 1998 1 Online-Ressource (XX, 355 p) txt rdacontent c rdamedia cr rdacarrier We wrote this book to introduce graduate students and research workers in var ious scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. In its fully developed form, the information-theoretic approach allows inference based on more than one model (including estimates of unconditional precision); in its initial form, it is useful in selecting a "best" model and ranking the remaining models. We believe that often the critical issue in data analysis is the selection of a good approximating model that best represents the inference supported by the data (an estimated "best approximating model"). In formation theory includes the well-known Kullback-Leibler "distance" between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) Kullback-Leibler distance based on Fisher's maximized log-likelihood. His mea sure, now called Akaike 's information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a funda mental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. We do not accept the notion that there is a simple, "true model" in the biological sciences Statistics Mathematical statistics Statistical Theory and Methods Statistik Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Biologie (DE-588)4006851-1 gnd rswk-swf Biologie (DE-588)4006851-1 s Mathematisches Modell (DE-588)4114528-8 s 1\p DE-604 Anderson, David R. Sonstige oth https://doi.org/10.1007/978-1-4757-2917-7 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Burnham, Kenneth P. Model Selection and Inference A Practical Information-Theoretic Approach Statistics Mathematical statistics Statistical Theory and Methods Statistik Mathematisches Modell (DE-588)4114528-8 gnd Biologie (DE-588)4006851-1 gnd |
| subject_GND | (DE-588)4114528-8 (DE-588)4006851-1 |
| title | Model Selection and Inference A Practical Information-Theoretic Approach |
| title_auth | Model Selection and Inference A Practical Information-Theoretic Approach |
| title_exact_search | Model Selection and Inference A Practical Information-Theoretic Approach |
| title_full | Model Selection and Inference A Practical Information-Theoretic Approach by Kenneth P. Burnham, David R. Anderson |
| title_fullStr | Model Selection and Inference A Practical Information-Theoretic Approach by Kenneth P. Burnham, David R. Anderson |
| title_full_unstemmed | Model Selection and Inference A Practical Information-Theoretic Approach by Kenneth P. Burnham, David R. Anderson |
| title_short | Model Selection and Inference |
| title_sort | model selection and inference a practical information theoretic approach |
| title_sub | A Practical Information-Theoretic Approach |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistik Mathematisches Modell (DE-588)4114528-8 gnd Biologie (DE-588)4006851-1 gnd |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistik Mathematisches Modell Biologie |
| url | https://doi.org/10.1007/978-1-4757-2917-7 |
| work_keys_str_mv | AT burnhamkennethp modelselectionandinferenceapracticalinformationtheoreticapproach AT andersondavidr modelselectionandinferenceapracticalinformationtheoreticapproach |