課程資訊

Computing in Epidemiology and Biostatistics

106-1

EPM5002

849 U0310

2.0

Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1061EPM5002_Comp

In most epidemiology and Biostatistic courses, we usually introduce the theoretical models, and then use some software (such as SAS and R) to analyze the data. However, there is a black box between these two parts. To fill in the gap, we will teach the numerical computation process behind statistical models. Students will learn matrix operations, numerical analyses, Monte-Carlo simulations, etc. We will teach how to construct a log-likelihood function based on a statistical distribution, how to obtain maximum likelihood estimates, how to build exact confidence intervals, by functions written by students themselves (rather than using some built-in function in R or SAS).

Students will learn matrix operations, numerical analyses, Monte-Carlo simulations, etc. We will teach how to construct a log-likelihood function based on a statistical distribution, how to obtain maximum likelihood estimates, how to build exact confidence intervals, by functions written by students themselves (rather than using some built-in function in R or SAS).

Prerequisite course: at least one biostatistics (or statistics) or epidemiology course.

Office Hours

1. Owen Jones, Robert Maillardet, and Andrew Robinson (2009). “Introduction to scientific programming and simulation using R”. Chapman & Hall/CRC.
2. Marcello Pagano and Kimberlee Gauvreau (2000). “Principles of Biostatistics”. 2nd edition. Duxbury Press.

(僅供參考)

 No. 項目 百分比 說明 1. 課堂參與及表現 40% Class participation and discussions 2. 作業 60% Assignments and homework

 課程進度
 週次 日期 單元主題 第1週 09/11 R 的安裝與起始 (林菀俞老師) Install and start R 第2週 09/18 函數運算 (林菀俞老師) Programming with functions 第3週 09/25 物件型態、程式邏輯與迴圈運算、R的輸入輸出與繪圖 (林菀俞老師) R objects, programming logics, R looping, input and output 第4週 10/02 R的輸入輸出與繪圖、apply家族函數與遞迴程式、矩陣運算指令 (林菀俞老師) R input and output, the clever “apply” family, recursive programming, matrix operations 第5週 10/09 調整放假 (於期中考週補課) Holiday 第6週 10/16 認識矩陣 (李文宗老師) Introduction to matrix algebra 第7週 10/23 矩陣運算 (李文宗老師) Matrix operations and linear algebra 第8週 10/30 矩陣代數的妙用 (李文宗老師) Matrix algebra in statistics 第9週 11/06 求函數解與確切信賴區間 (林菀俞老師) Find the root of a function; exact confidence intervals 第10週 11/13 建構概似函數 (林菀俞老師) Construct a log-likelihood function 第11週 11/20 求解最大概似估計值 (I) (林菀俞老師) Find the maximum likelihood estimates for a logistic regression 第12週 11/27 求解最大概似估計值 (II) (林菀俞老師) Find the maximum likelihood estimates for a Poisson regression 第13週 12/04 現代統計計算：蒙地卡羅模擬 (I) (林菀俞老師) Modern statistical computing in R: Monte-Carlo simulations (Point estimates) 第14週 12/11 現代統計計算：蒙地卡羅模擬 (II) (林菀俞老師) Modern statistical computing in R: Monte-Carlo simulations (Interval estimates) 第15週 12/18 現代統計計算：蒙地卡羅模擬 (III) (林菀俞老師) Modern statistical computing in R: Monte-Carlo simulations (Type I error rates and power) 第16週 12/25 現代統計計算：排列法(permutation)於統計檢定上之妙用 (林菀俞老師) Modern statistical computing in R: Permutations – theory and applications 第17週 2018/01/01 開國紀念日 (放假日) Holiday 第18週 2018/01/08 期末考週 Final-exam week