Selected article for: "likelihood estimation and MLE likelihood estimation"

Author: Pei Wang; Junan Lu; Yanyu Jin; Mengfan Zhu; Lingling Wang; Shunjie Chen
Title: Epidemiological characteristics of 1212 COVID-19 patients in Henan, China
  • Document date: 2020_2_23
  • ID: 1mxjklgx_26
    Snippet: Here, µ, σ 2 are two parameters that can be estimated from data. We consider three different approaches to estimate the parameters µ and σ 2 , including the moment estimation (ME), the ordinary least square (OLS) estimation and the maximum likelihood estimation (MLE) [34] . As to the ME, based on the PDF of incubation period τ , we can easily obtain E(τ ) = exp µ + σ 2 /2 , D(τ ) = (e σ 2 − 1)exp 2µ + σ 2 . Using the fsolve function.....
    Document: Here, µ, σ 2 are two parameters that can be estimated from data. We consider three different approaches to estimate the parameters µ and σ 2 , including the moment estimation (ME), the ordinary least square (OLS) estimation and the maximum likelihood estimation (MLE) [34] . As to the ME, based on the PDF of incubation period τ , we can easily obtain E(τ ) = exp µ + σ 2 /2 , D(τ ) = (e σ 2 − 1)exp 2µ + σ 2 . Using the fsolve function (which is based on the OLS method) in Matlab, we obtainμ m = 1.8239, σ 2 m = 0.3629. The fitted PDF curve can be found in Fig.3 A, and some theoretical estimations of the incubation period based on the ME method are summarized in Tab.1.

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