Statistical inference: the minimum distance approach
"Preface In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. A parametric model imposes a certain structure on the class of probability distributions that may be used to describe real life data generated from a process under study....
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, FL [u.a.]
CRC Press
2011
|
| Schriftenreihe: | Monographs on statistics and applied probability
120 |
| Schlagworte: | |
| Zusammenfassung: | "Preface In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. A parametric model imposes a certain structure on the class of probability distributions that may be used to describe real life data generated from a process under study. There hardly appears to be a better way to deal with such a problem than to choose the parametric model that minimizes an appropriately defined distance between the data and the model. The issue is an important and complex one. There are many different ways of constructing an appropriate "distance" between the "data" and the "model". One could, for example, construct a distance between the empirical distribution function and the model distribution function by a suitable measure of distance. Alternatively, one could minimize the distance between the estimated data density (obtained, if necessary, by using a nonparametric smoothing technique such as kernel density estimation) and the parametric model density. And when the particular nature of the distances have been settled (based on distribution functions, based on densities, etc.), there may be innumerable options for the distance to be used within the particular type of distances. So the scope of study referred to by "Minimum Distance Estimation" is literally huge"--Provided by publisher |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xix, 409 p. graph. Darst. 25 cm |
| ISBN: | 9781420099652 1420099655 |
Internformat
MARC
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| 245 | 1 | 0 | |a Statistical inference |b the minimum distance approach |c Ayanendranath Basu ; Hiroyuki Shioya ; Chanseok Park |
| 264 | 1 | |a Boca Raton, FL [u.a.] |b CRC Press |c 2011 | |
| 300 | |a xix, 409 p. |b graph. Darst. |c 25 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Monographs on statistics and applied probability |v 120 | |
| 500 | |a Includes bibliographical references and index | ||
| 520 | |a "Preface In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. A parametric model imposes a certain structure on the class of probability distributions that may be used to describe real life data generated from a process under study. There hardly appears to be a better way to deal with such a problem than to choose the parametric model that minimizes an appropriately defined distance between the data and the model. The issue is an important and complex one. There are many different ways of constructing an appropriate "distance" between the "data" and the "model". One could, for example, construct a distance between the empirical distribution function and the model distribution function by a suitable measure of distance. Alternatively, one could minimize the distance between the estimated data density (obtained, if necessary, by using a nonparametric smoothing technique such as kernel density estimation) and the parametric model density. And when the particular nature of the distances have been settled (based on distribution functions, based on densities, etc.), there may be innumerable options for the distance to be used within the particular type of distances. So the scope of study referred to by "Minimum Distance Estimation" is literally huge"--Provided by publisher | ||
| 650 | 4 | |a Estimation theory | |
| 650 | 4 | |a Distances | |
| 650 | 7 | |a COMPUTERS / General |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS / Probability & Statistics / General |2 bisacsh | |
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| 700 | 1 | |a Shioya, Hiroyuki |e Verfasser |4 aut | |
| 700 | 1 | |a Park, Chanseok |e Verfasser |4 aut | |
| 830 | 0 | |a Monographs on statistics and applied probability |v 120 |w (DE-604)BV002494005 |9 120 | |
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Datensatz im Suchindex
| _version_ | 1804147981428981760 |
|---|---|
| any_adam_object | |
| author | Basu, Ayanendranath Shioya, Hiroyuki Park, Chanseok |
| author_GND | (DE-588)171691407 |
| author_facet | Basu, Ayanendranath Shioya, Hiroyuki Park, Chanseok |
| author_role | aut aut aut |
| author_sort | Basu, Ayanendranath |
| author_variant | a b ab h s hs c p cp |
| building | Verbundindex |
| bvnumber | BV039141694 |
| classification_rvk | QH 231 |
| classification_tum | MAT 625f |
| ctrlnum | (OCoLC)739645906 (DE-599)BVBBV039141694 |
| dewey-full | 519.5/44 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/44 |
| dewey-search | 519.5/44 |
| dewey-sort | 3519.5 244 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV039141694 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T23:59:52Z |
| institution | BVB |
| isbn | 9781420099652 1420099655 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024159713 |
| oclc_num | 739645906 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-29T DE-N2 DE-83 |
| owner_facet | DE-91G DE-BY-TUM DE-29T DE-N2 DE-83 |
| physical | xix, 409 p. graph. Darst. 25 cm |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | CRC Press |
| record_format | marc |
| series | Monographs on statistics and applied probability |
| series2 | Monographs on statistics and applied probability |
| spelling | Basu, Ayanendranath Verfasser (DE-588)171691407 aut Statistical inference the minimum distance approach Ayanendranath Basu ; Hiroyuki Shioya ; Chanseok Park Boca Raton, FL [u.a.] CRC Press 2011 xix, 409 p. graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Monographs on statistics and applied probability 120 Includes bibliographical references and index "Preface In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. A parametric model imposes a certain structure on the class of probability distributions that may be used to describe real life data generated from a process under study. There hardly appears to be a better way to deal with such a problem than to choose the parametric model that minimizes an appropriately defined distance between the data and the model. The issue is an important and complex one. There are many different ways of constructing an appropriate "distance" between the "data" and the "model". One could, for example, construct a distance between the empirical distribution function and the model distribution function by a suitable measure of distance. Alternatively, one could minimize the distance between the estimated data density (obtained, if necessary, by using a nonparametric smoothing technique such as kernel density estimation) and the parametric model density. And when the particular nature of the distances have been settled (based on distribution functions, based on densities, etc.), there may be innumerable options for the distance to be used within the particular type of distances. So the scope of study referred to by "Minimum Distance Estimation" is literally huge"--Provided by publisher Estimation theory Distances COMPUTERS / General bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Inferenzstatistik (DE-588)4247120-5 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 s DE-604 Inferenzstatistik (DE-588)4247120-5 s Shioya, Hiroyuki Verfasser aut Park, Chanseok Verfasser aut Monographs on statistics and applied probability 120 (DE-604)BV002494005 120 |
| spellingShingle | Basu, Ayanendranath Shioya, Hiroyuki Park, Chanseok Statistical inference the minimum distance approach Monographs on statistics and applied probability Estimation theory Distances COMPUTERS / General bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Inferenzstatistik (DE-588)4247120-5 gnd Statistische Schlussweise (DE-588)4182963-3 gnd |
| subject_GND | (DE-588)4247120-5 (DE-588)4182963-3 |
| title | Statistical inference the minimum distance approach |
| title_auth | Statistical inference the minimum distance approach |
| title_exact_search | Statistical inference the minimum distance approach |
| title_full | Statistical inference the minimum distance approach Ayanendranath Basu ; Hiroyuki Shioya ; Chanseok Park |
| title_fullStr | Statistical inference the minimum distance approach Ayanendranath Basu ; Hiroyuki Shioya ; Chanseok Park |
| title_full_unstemmed | Statistical inference the minimum distance approach Ayanendranath Basu ; Hiroyuki Shioya ; Chanseok Park |
| title_short | Statistical inference |
| title_sort | statistical inference the minimum distance approach |
| title_sub | the minimum distance approach |
| topic | Estimation theory Distances COMPUTERS / General bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Inferenzstatistik (DE-588)4247120-5 gnd Statistische Schlussweise (DE-588)4182963-3 gnd |
| topic_facet | Estimation theory Distances COMPUTERS / General MATHEMATICS / Probability & Statistics / General Inferenzstatistik Statistische Schlussweise |
| volume_link | (DE-604)BV002494005 |
| work_keys_str_mv | AT basuayanendranath statisticalinferencetheminimumdistanceapproach AT shioyahiroyuki statisticalinferencetheminimumdistanceapproach AT parkchanseok statisticalinferencetheminimumdistanceapproach |