東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Estimating Parameters of the Sir Epi...

Hull-Nye, Dylan.

FindBook

Google Book

Amazon

博客來

Estimating Parameters of the Sir Epidemiology Model Using Simulated Survey Data and Multinomial Maximum Likelihood.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Estimating Parameters of the Sir Epidemiology Model Using Simulated Survey Data and Multinomial Maximum Likelihood./
作者:	Hull-Nye, Dylan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	42 p.
附註:	Source: Masters Abstracts International, Volume: 82-03.
Contained By:	Masters Abstracts International82-03.
標題:	Statistics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27964207
ISBN:	9798664796896

Estimating Parameters of the Sir Epidemiology Model Using Simulated Survey Data and Multinomial Maximum Likelihood.
Hull-Nye, Dylan.

Estimating Parameters of the Sir Epidemiology Model Using Simulated Survey Data and Multinomial Maximum Likelihood. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 42 p.

Source: Masters Abstracts International, Volume: 82-03.

Thesis (M.S.)--University of Idaho, 2020.

This item must not be sold to any third party vendors.

Brian Dennis and William Kemp (et al.) in "Stochastic Model of Insect Phenology Estimation and Testing" use a Multinomial Maximum likelihood approach to estimate model parameters via ratios between variables. The model in this paper attempts to estimate deterministic epidemiological population parameters for the Kermack-McKendrick SIR model using simulated sample survey data. Ratios between populations in the SIR model are used as probabilities in the Multinomial distribution and resulting estimates of population paarameters are analyzed via Bootstrap confidence intervals, visual analysis, and mean squared error (MSE) estimates. The results show that certain surveys schemes perform better than others based on survey sample size, number of surveys, and survey spacing. All simulations generally produce unbiased estimates with some producing smaller variances than others. Applications include the estimating of infectious disease dynamics using small survey sampling in rural or small town universities and populations.

ISBN: 9798664796896Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Sir Epidemiology Model

Estimating Parameters of the Sir Epidemiology Model Using Simulated Survey Data and Multinomial Maximum Likelihood.
LDR:02158nmm a2200349 4500 001 2278052
005 20210611082011.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798664796896
035 $a (MiAaPQ)AAI27964207
035 $a AAI27964207
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hull-Nye, Dylan. $3 3556414
245 1 0 $a Estimating Parameters of the Sir Epidemiology Model Using Simulated Survey Data and Multinomial Maximum Likelihood.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 42 p.
500 $a Source: Masters Abstracts International, Volume: 82-03.
500 $a Advisor: Dennis, Brian.
502 $a Thesis (M.S.)--University of Idaho, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Brian Dennis and William Kemp (et al.) in "Stochastic Model of Insect Phenology Estimation and Testing" use a Multinomial Maximum likelihood approach to estimate model parameters via ratios between variables. The model in this paper attempts to estimate deterministic epidemiological population parameters for the Kermack-McKendrick SIR model using simulated sample survey data. Ratios between populations in the SIR model are used as probabilities in the Multinomial distribution and resulting estimates of population paarameters are analyzed via Bootstrap confidence intervals, visual analysis, and mean squared error (MSE) estimates. The results show that certain surveys schemes perform better than others based on survey sample size, number of surveys, and survey spacing. All simulations generally produce unbiased estimates with some producing smaller variances than others. Applications include the estimating of infectious disease dynamics using small survey sampling in rural or small town universities and populations.
590 $a School code: 0089.
650 4 $a Statistics. $3 517247
650 4 $a Applied mathematics. $3 2122814
650 4 $a Epidemiology. $3 568544
653 $a Sir Epidemiology Model
653 $a Simulated survey data
653 $a Multinomial maximum likelihood
690 $a 0463
690 $a 0364
690 $a 0766
710 2 $a University of Idaho. $b Statistical Sciences. $3 3179439
773 0 $t Masters Abstracts International $g 82-03.
790 $a 0089
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27964207