Smoothing Methods in Statistics:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1996
|
| Series: | Springer Series in Statistics
|
| Subjects: | |
| Online Access: | Volltext |
| Item Description: | The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue |
| Physical Description: | 1 Online-Ressource (XII, 340 p) |
| ISBN: | 9781461240266 9781461284727 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-1-4612-4026-6 |
Staff View
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV042420191 | ||
| 003 | DE-604 | ||
| 005 | 20220103 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150317s1996 |||| o||u| ||||||eng d | ||
| 020 | |a 9781461240266 |c Online |9 978-1-4612-4026-6 | ||
| 020 | |a 9781461284727 |c Print |9 978-1-4612-8472-7 | ||
| 024 | 7 | |a 10.1007/978-1-4612-4026-6 |2 doi | |
| 035 | |a (OCoLC)863786785 | ||
| 035 | |a (DE-599)BVBBV042420191 | ||
| 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 519.2 |2 23 | |
| 084 | |a MAT 000 |2 stub | ||
| 084 | |a 62P99 |2 msc | ||
| 084 | |a 62G05 |2 msc | ||
| 084 | |a 62G07 |2 msc | ||
| 100 | 1 | |a Simonoff, Jeffrey S. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Smoothing Methods in Statistics |c by Jeffrey S. Simonoff |
| 264 | 1 | |a New York, NY |b Springer New York |c 1996 | |
| 300 | |a 1 Online-Ressource (XII, 340 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Springer Series in Statistics |x 0172-7397 | |
| 500 | |a The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue | ||
| 650 | 4 | |a Mathematics | |
| 650 | 4 | |a Distribution (Probability theory) | |
| 650 | 4 | |a Probability Theory and Stochastic Processes | |
| 650 | 4 | |a Mathematik | |
| 650 | 0 | 7 | |a Glättung |0 (DE-588)4157404-7 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Glättung |0 (DE-588)4157404-7 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4612-4026-6 |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:BVB01-027855608 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Record in the Search Index
| _version_ | 1804153091806724096 |
|---|---|
| any_adam_object | |
| author | Simonoff, Jeffrey S. |
| author_facet | Simonoff, Jeffrey S. |
| author_role | aut |
| author_sort | Simonoff, Jeffrey S. |
| author_variant | j s s js jss |
| building | Verbundindex |
| bvnumber | BV042420191 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)863786785 (DE-599)BVBBV042420191 |
| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4612-4026-6 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02745nmm a2200493zc 4500</leader><controlfield tag="001">BV042420191</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220103 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150317s1996 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781461240266</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-4612-4026-6</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781461284727</subfield><subfield code="c">Print</subfield><subfield code="9">978-1-4612-8472-7</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4612-4026-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)863786785</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042420191</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">519.2</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="084" ind1=" " ind2=" "><subfield code="a">62P99</subfield><subfield code="2">msc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">62G05</subfield><subfield code="2">msc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">62G07</subfield><subfield code="2">msc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Simonoff, Jeffrey S.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Smoothing Methods in Statistics</subfield><subfield code="c">by Jeffrey S. Simonoff</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">1996</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XII, 340 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">Springer Series in Statistics</subfield><subfield code="x">0172-7397</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distribution (Probability theory)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Probability Theory and Stochastic Processes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematik</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Glättung</subfield><subfield code="0">(DE-588)4157404-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Glättung</subfield><subfield code="0">(DE-588)4157404-7</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="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4612-4026-6</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:BVB01-027855608</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.BV042420191 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:06Z |
| institution | BVB |
| isbn | 9781461240266 9781461284727 |
| issn | 0172-7397 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027855608 |
| oclc_num | 863786785 |
| 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 (XII, 340 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | Springer New York |
| record_format | marc |
| series2 | Springer Series in Statistics |
| spelling | Simonoff, Jeffrey S. Verfasser aut Smoothing Methods in Statistics by Jeffrey S. Simonoff New York, NY Springer New York 1996 1 Online-Ressource (XII, 340 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Glättung (DE-588)4157404-7 gnd rswk-swf Glättung (DE-588)4157404-7 s 1\p DE-604 https://doi.org/10.1007/978-1-4612-4026-6 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Simonoff, Jeffrey S. Smoothing Methods in Statistics Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Glättung (DE-588)4157404-7 gnd |
| subject_GND | (DE-588)4157404-7 |
| title | Smoothing Methods in Statistics |
| title_auth | Smoothing Methods in Statistics |
| title_exact_search | Smoothing Methods in Statistics |
| title_full | Smoothing Methods in Statistics by Jeffrey S. Simonoff |
| title_fullStr | Smoothing Methods in Statistics by Jeffrey S. Simonoff |
| title_full_unstemmed | Smoothing Methods in Statistics by Jeffrey S. Simonoff |
| title_short | Smoothing Methods in Statistics |
| title_sort | smoothing methods in statistics |
| topic | Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Glättung (DE-588)4157404-7 gnd |
| topic_facet | Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Glättung |
| url | https://doi.org/10.1007/978-1-4612-4026-6 |
| work_keys_str_mv | AT simonoffjeffreys smoothingmethodsinstatistics |