Statistical applications using fuzzy sets:
Despite considerable interest of statisticians of all kinds in high-dimensional, sparse, categorical data, the standard methods for dealing with this interest have specific limitations. One approach, the factor analysis of tetrachoric correlation, often falls prey to the use of incorrect approximati...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York u.a.
Wiley
1994
|
| Schriftenreihe: | Wiley series in probability and mathematical statistics : Probability and mathematical statistics
A Wiley-Interscience publication |
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of Contents |
| Zusammenfassung: | Despite considerable interest of statisticians of all kinds in high-dimensional, sparse, categorical data, the standard methods for dealing with this interest have specific limitations. One approach, the factor analysis of tetrachoric correlation, often falls prey to the use of incorrect approximating assumptions. Another, latent structure analysis, can become computational refractory, except for problems with fewest cases and variables Now there's a third approach using a new strategy for resolving measure theoretic issues involving this type of data. That approach centers on the fuzzy set or fuzzy partition models generated by convex geometrical sets. Originally developed in electrical engineering, these models have been finding a growing number of applications in computer science, physics, and theoretical biology. This popularity stems from the power of fuzzy set models to vastly improve on the approximation of the infinite dimensionality and heterogeneity of the real world that arises from the use of statistical partitions, no matter how fine In this unique book, these models are applied to concrete data from the National Long Term Care Surveys, the National Channeling Demonstration, the Social/HMO Demonstration, the California MSSP Study, and more. In each case the results are compared to the alternative, competing analytic procedures, such as latent class analysis, and are shown to fit the data better, provide substantively more meaningful results, and generate excellent predictions of external variables not used to form the basic dimensions of the model. The models are also shown to be able to predict Medicare and private health expenditures, mortality and morbidity risks, and health services use, as well as provide a high measure of clinical meaningfulness for medical and nursing experts. Numerous tables are also provided, showing the results of specific analyses and illustrating how the parametric structure of the models identifies critical features of the data set |
| Beschreibung: | XI, 312 S. |
| ISBN: | 0471545619 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV010106666 | ||
| 003 | DE-604 | ||
| 005 | 20150309 | ||
| 007 | t | ||
| 008 | 950321s1994 xxu |||| 00||| eng d | ||
| 020 | |a 0471545619 |9 0-471-54561-9 | ||
| 035 | |a (OCoLC)28256158 | ||
| 035 | |a (DE-599)BVBBV010106666 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c XD-US | ||
| 049 | |a DE-739 |a DE-703 |a DE-355 |a DE-521 |a DE-83 |a DE-11 |a DE-188 | ||
| 050 | 0 | |a QA248.M285 1994 | |
| 082 | 0 | |a 519.5 20 | |
| 082 | 0 | |a 519.5 |2 20 | |
| 084 | |a QH 233 |0 (DE-625)141548: |2 rvk | ||
| 084 | |a SK 830 |0 (DE-625)143259: |2 rvk | ||
| 084 | |a SK 850 |0 (DE-625)143263: |2 rvk | ||
| 100 | 1 | |a Manton, Kenneth G. |d 1947- |e Verfasser |0 (DE-588)128703849 |4 aut | |
| 245 | 1 | 0 | |a Statistical applications using fuzzy sets |c Kenneth G. Manton ; Max A. Woodbury ; H. Dennis Tolley |
| 264 | 1 | |a New York u.a. |b Wiley |c 1994 | |
| 300 | |a XI, 312 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and mathematical statistics : Probability and mathematical statistics | |
| 490 | 0 | |a A Wiley-Interscience publication | |
| 520 | 3 | |a Despite considerable interest of statisticians of all kinds in high-dimensional, sparse, categorical data, the standard methods for dealing with this interest have specific limitations. One approach, the factor analysis of tetrachoric correlation, often falls prey to the use of incorrect approximating assumptions. Another, latent structure analysis, can become computational refractory, except for problems with fewest cases and variables | |
| 520 | |a Now there's a third approach using a new strategy for resolving measure theoretic issues involving this type of data. That approach centers on the fuzzy set or fuzzy partition models generated by convex geometrical sets. Originally developed in electrical engineering, these models have been finding a growing number of applications in computer science, physics, and theoretical biology. This popularity stems from the power of fuzzy set models to vastly improve on the approximation of the infinite dimensionality and heterogeneity of the real world that arises from the use of statistical partitions, no matter how fine | ||
| 520 | |a In this unique book, these models are applied to concrete data from the National Long Term Care Surveys, the National Channeling Demonstration, the Social/HMO Demonstration, the California MSSP Study, and more. In each case the results are compared to the alternative, competing analytic procedures, such as latent class analysis, and are shown to fit the data better, provide substantively more meaningful results, and generate excellent predictions of external variables not used to form the basic dimensions of the model. The models are also shown to be able to predict Medicare and private health expenditures, mortality and morbidity risks, and health services use, as well as provide a high measure of clinical meaningfulness for medical and nursing experts. Numerous tables are also provided, showing the results of specific analyses and illustrating how the parametric structure of the models identifies critical features of the data set | ||
| 650 | 7 | |a Ensembles flous |2 ram | |
| 650 | 7 | |a Fuzzy sets |2 gtt | |
| 650 | 7 | |a Statistique mathématique |2 ram | |
| 650 | 7 | |a Statistische methoden |2 gtt | |
| 650 | 4 | |a Fuzzy sets | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Fuzzy-Menge |0 (DE-588)4061868-7 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Fuzzy-Menge |0 (DE-588)4061868-7 |D s |
| 689 | 0 | 1 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Woodbury, Max A. |e Verfasser |0 (DE-588)171009878 |4 aut | |
| 700 | 1 | |a Tolley, H. D. |e Verfasser |0 (DE-588)17060828X |4 aut | |
| 856 | 4 | |u http://www.loc.gov/catdir/description/wiley033/93002324.html |3 Publisher description | |
| 856 | 4 | |u http://www.loc.gov/catdir/toc/onix04/93002324.html |3 Table of Contents | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-006710373 | ||
Datensatz im Suchindex
| _version_ | 1804124498094456832 |
|---|---|
| any_adam_object | |
| author | Manton, Kenneth G. 1947- Woodbury, Max A. Tolley, H. D. |
| author_GND | (DE-588)128703849 (DE-588)171009878 (DE-588)17060828X |
| author_facet | Manton, Kenneth G. 1947- Woodbury, Max A. Tolley, H. D. |
| author_role | aut aut aut |
| author_sort | Manton, Kenneth G. 1947- |
| author_variant | k g m kg kgm m a w ma maw h d t hd hdt |
| building | Verbundindex |
| bvnumber | BV010106666 |
| callnumber-first | Q - Science |
| callnumber-label | QA248 |
| callnumber-raw | QA248.M285 1994 |
| callnumber-search | QA248.M285 1994 |
| callnumber-sort | QA 3248 M285 41994 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 233 SK 830 SK 850 |
| ctrlnum | (OCoLC)28256158 (DE-599)BVBBV010106666 |
| dewey-full | 519.520 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 20 519.5 |
| dewey-search | 519.5 20 519.5 |
| dewey-sort | 3519.5 220 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04065nam a2200577 c 4500</leader><controlfield tag="001">BV010106666</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20150309 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">950321s1994 xxu |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0471545619</subfield><subfield code="9">0-471-54561-9</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)28256158</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV010106666</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">XD-US</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-739</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-521</subfield><subfield code="a">DE-83</subfield><subfield code="a">DE-11</subfield><subfield code="a">DE-188</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA248.M285 1994</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5 20</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5</subfield><subfield code="2">20</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 233</subfield><subfield code="0">(DE-625)141548:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 830</subfield><subfield code="0">(DE-625)143259:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 850</subfield><subfield code="0">(DE-625)143263:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Manton, Kenneth G.</subfield><subfield code="d">1947-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)128703849</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Statistical applications using fuzzy sets</subfield><subfield code="c">Kenneth G. Manton ; Max A. Woodbury ; H. Dennis Tolley</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York u.a.</subfield><subfield code="b">Wiley</subfield><subfield code="c">1994</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XI, 312 S.</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">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Wiley series in probability and mathematical statistics : Probability and mathematical statistics</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">A Wiley-Interscience publication</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Despite considerable interest of statisticians of all kinds in high-dimensional, sparse, categorical data, the standard methods for dealing with this interest have specific limitations. One approach, the factor analysis of tetrachoric correlation, often falls prey to the use of incorrect approximating assumptions. Another, latent structure analysis, can become computational refractory, except for problems with fewest cases and variables</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Now there's a third approach using a new strategy for resolving measure theoretic issues involving this type of data. That approach centers on the fuzzy set or fuzzy partition models generated by convex geometrical sets. Originally developed in electrical engineering, these models have been finding a growing number of applications in computer science, physics, and theoretical biology. This popularity stems from the power of fuzzy set models to vastly improve on the approximation of the infinite dimensionality and heterogeneity of the real world that arises from the use of statistical partitions, no matter how fine</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this unique book, these models are applied to concrete data from the National Long Term Care Surveys, the National Channeling Demonstration, the Social/HMO Demonstration, the California MSSP Study, and more. In each case the results are compared to the alternative, competing analytic procedures, such as latent class analysis, and are shown to fit the data better, provide substantively more meaningful results, and generate excellent predictions of external variables not used to form the basic dimensions of the model. The models are also shown to be able to predict Medicare and private health expenditures, mortality and morbidity risks, and health services use, as well as provide a high measure of clinical meaningfulness for medical and nursing experts. Numerous tables are also provided, showing the results of specific analyses and illustrating how the parametric structure of the models identifies critical features of the data set</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ensembles flous</subfield><subfield code="2">ram</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fuzzy sets</subfield><subfield code="2">gtt</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Statistique mathématique</subfield><subfield code="2">ram</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Statistische methoden</subfield><subfield code="2">gtt</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical statistics</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Fuzzy-Menge</subfield><subfield code="0">(DE-588)4061868-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Fuzzy-Menge</subfield><subfield code="0">(DE-588)4061868-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Woodbury, Max A.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)171009878</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tolley, H. D.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)17060828X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2=" "><subfield code="u">http://www.loc.gov/catdir/description/wiley033/93002324.html</subfield><subfield code="3">Publisher description</subfield></datafield><datafield tag="856" ind1="4" ind2=" "><subfield code="u">http://www.loc.gov/catdir/toc/onix04/93002324.html</subfield><subfield code="3">Table of Contents</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-006710373</subfield></datafield></record></collection> |
| id | DE-604.BV010106666 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:46:37Z |
| institution | BVB |
| isbn | 0471545619 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006710373 |
| oclc_num | 28256158 |
| open_access_boolean | |
| owner | DE-739 DE-703 DE-355 DE-BY-UBR DE-521 DE-83 DE-11 DE-188 |
| owner_facet | DE-739 DE-703 DE-355 DE-BY-UBR DE-521 DE-83 DE-11 DE-188 |
| physical | XI, 312 S. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and mathematical statistics : Probability and mathematical statistics A Wiley-Interscience publication |
| spelling | Manton, Kenneth G. 1947- Verfasser (DE-588)128703849 aut Statistical applications using fuzzy sets Kenneth G. Manton ; Max A. Woodbury ; H. Dennis Tolley New York u.a. Wiley 1994 XI, 312 S. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and mathematical statistics : Probability and mathematical statistics A Wiley-Interscience publication Despite considerable interest of statisticians of all kinds in high-dimensional, sparse, categorical data, the standard methods for dealing with this interest have specific limitations. One approach, the factor analysis of tetrachoric correlation, often falls prey to the use of incorrect approximating assumptions. Another, latent structure analysis, can become computational refractory, except for problems with fewest cases and variables Now there's a third approach using a new strategy for resolving measure theoretic issues involving this type of data. That approach centers on the fuzzy set or fuzzy partition models generated by convex geometrical sets. Originally developed in electrical engineering, these models have been finding a growing number of applications in computer science, physics, and theoretical biology. This popularity stems from the power of fuzzy set models to vastly improve on the approximation of the infinite dimensionality and heterogeneity of the real world that arises from the use of statistical partitions, no matter how fine In this unique book, these models are applied to concrete data from the National Long Term Care Surveys, the National Channeling Demonstration, the Social/HMO Demonstration, the California MSSP Study, and more. In each case the results are compared to the alternative, competing analytic procedures, such as latent class analysis, and are shown to fit the data better, provide substantively more meaningful results, and generate excellent predictions of external variables not used to form the basic dimensions of the model. The models are also shown to be able to predict Medicare and private health expenditures, mortality and morbidity risks, and health services use, as well as provide a high measure of clinical meaningfulness for medical and nursing experts. Numerous tables are also provided, showing the results of specific analyses and illustrating how the parametric structure of the models identifies critical features of the data set Ensembles flous ram Fuzzy sets gtt Statistique mathématique ram Statistische methoden gtt Fuzzy sets Mathematical statistics Statistik (DE-588)4056995-0 gnd rswk-swf Fuzzy-Menge (DE-588)4061868-7 gnd rswk-swf Fuzzy-Menge (DE-588)4061868-7 s Statistik (DE-588)4056995-0 s DE-604 Woodbury, Max A. Verfasser (DE-588)171009878 aut Tolley, H. D. Verfasser (DE-588)17060828X aut http://www.loc.gov/catdir/description/wiley033/93002324.html Publisher description http://www.loc.gov/catdir/toc/onix04/93002324.html Table of Contents |
| spellingShingle | Manton, Kenneth G. 1947- Woodbury, Max A. Tolley, H. D. Statistical applications using fuzzy sets Ensembles flous ram Fuzzy sets gtt Statistique mathématique ram Statistische methoden gtt Fuzzy sets Mathematical statistics Statistik (DE-588)4056995-0 gnd Fuzzy-Menge (DE-588)4061868-7 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4061868-7 |
| title | Statistical applications using fuzzy sets |
| title_auth | Statistical applications using fuzzy sets |
| title_exact_search | Statistical applications using fuzzy sets |
| title_full | Statistical applications using fuzzy sets Kenneth G. Manton ; Max A. Woodbury ; H. Dennis Tolley |
| title_fullStr | Statistical applications using fuzzy sets Kenneth G. Manton ; Max A. Woodbury ; H. Dennis Tolley |
| title_full_unstemmed | Statistical applications using fuzzy sets Kenneth G. Manton ; Max A. Woodbury ; H. Dennis Tolley |
| title_short | Statistical applications using fuzzy sets |
| title_sort | statistical applications using fuzzy sets |
| topic | Ensembles flous ram Fuzzy sets gtt Statistique mathématique ram Statistische methoden gtt Fuzzy sets Mathematical statistics Statistik (DE-588)4056995-0 gnd Fuzzy-Menge (DE-588)4061868-7 gnd |
| topic_facet | Ensembles flous Fuzzy sets Statistique mathématique Statistische methoden Mathematical statistics Statistik Fuzzy-Menge |
| url | http://www.loc.gov/catdir/description/wiley033/93002324.html http://www.loc.gov/catdir/toc/onix04/93002324.html |
| work_keys_str_mv | AT mantonkennethg statisticalapplicationsusingfuzzysets AT woodburymaxa statisticalapplicationsusingfuzzysets AT tolleyhd statisticalapplicationsusingfuzzysets |