Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch13
{"title":"Models for Partially Classified Contingency Tables, Ignoring the Missingness Mechanism","authors":"","doi":"10.1002/9781119482260.ch13","DOIUrl":"https://doi.org/10.1002/9781119482260.ch13","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122545986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch7
{"title":"Factored Likelihood Methods When the Missingness Mechanism Is Ignorable","authors":"","doi":"10.1002/9781119482260.ch7","DOIUrl":"https://doi.org/10.1002/9781119482260.ch7","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116723267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch12
{"title":"Models for Robust Estimation","authors":"","doi":"10.1002/9781119482260.ch12","DOIUrl":"https://doi.org/10.1002/9781119482260.ch12","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122039990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch11
{"title":"Multivariate Normal Examples, Ignoring the Missingness Mechanism","authors":"","doi":"10.1002/9781119482260.ch11","DOIUrl":"https://doi.org/10.1002/9781119482260.ch11","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133302131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch5
{"title":"Accounting for Uncertainty from Missing Data","authors":"","doi":"10.1002/9781119482260.ch5","DOIUrl":"https://doi.org/10.1002/9781119482260.ch5","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125636759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch14
{"title":"Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missingness Mechanism","authors":"","doi":"10.1002/9781119482260.ch14","DOIUrl":"https://doi.org/10.1002/9781119482260.ch14","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"19 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133166359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch9
{"title":"Large‐Sample Inference Based on Maximum Likelihood Estimates","authors":"","doi":"10.1002/9781119482260.ch9","DOIUrl":"https://doi.org/10.1002/9781119482260.ch9","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"390 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115991337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-12DOI: 10.1002/9781119482260.ch15
{"title":"Missing Not at Random Models","authors":"","doi":"10.1002/9781119482260.ch15","DOIUrl":"https://doi.org/10.1002/9781119482260.ch15","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134277312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-08-28DOI: 10.1002/9781119013563.CH8
R. Little, D. Rubin
{"title":"Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse","authors":"R. Little, D. Rubin","doi":"10.1002/9781119013563.CH8","DOIUrl":"https://doi.org/10.1002/9781119013563.CH8","url":null,"abstract":"","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124336606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-08-28DOI: 10.1002/9781119013563.CH2
R. Little, D. Rubin
Application of simple methods Complete case analysis and variable mean imputation Download from the website, and into R a dataset called ceo.dat. The dataset contains information on 6 variables of fortune 500 companies in 1999. It has 447 cases. Originally, the fortune 500 consists of 500 cases, but 53 cases were deleted due to missing values. The six variables are • salary: 1999 CEO salary plus bonuses (thousand $) • totcomp: 1999 CEO total compensation (thousand $) • tenure: number of years as CEO (is 0 if less than 6 months) • age: age of CEO in years • sales: total 1998 sales revenue of firm i (million $) • profits: 1998 profits for firm i (million $) • assets: total assets of firm i in 1998 (million $) In this problem you will generate missing data yourself using three mechanisms (MCAR, MAR, NMAR). Next, you will apply two different missing data methods (complete case analysis and variable mean imputation), and compute means, variances, and the correlation matrix. Also, we are interested in salary as a function of profits (salary= a + b·profits). Since the data set is complete, and you generate missing data yourself, you will be able to compare the results from the two missing data methods to the population values (obtained from the complete data). We will assume that missing data occurs only the variables salary, totcomp, and age. The other three variables will not have missing values. Do the following: 1. Simulate 25% nonresponse in the three variables described; you will obtain three data sets, one for each scenario. Think about how you can get MCAR, MAR, and NMAR. 2. For each incomplete data set, compute the means, variances, the correlation matrix, and the regression described above. 3. Compare the results with the complete data means, variances, the correlation matrix, and the regression. 4. Write a report (2 page max) in which you describe the findings. Also describe how you generated MCAR, MAR, and NMAR, and why your method of generating nonresponse resulted in that particular mechanism. 6. Include R programs you wrote and R commands you used on a separate page. Use the materials from chapter 2, and the handout to find the least squares estimates for the missing observations in the data set called carsmiss. Download it from the website, and into R using the command carsmiss <-read.table("carsmiss.txt",T,sep=","). The data set carsmiss has four …
简单方法的应用完成案例分析和变量均值归算从网站下载，并导入一个名为ceo.dat的数据集。该数据集包含1999年财富500强企业的6个变量信息。它有447个病例。最初，财富500强由500个案例组成，但由于缺少价值而删除了53个案例。六个变量•工资:1999 CEO工资+奖金(千美元)•totcomp: 1999首席执行官的总薪酬(千美元)•任期:多年担任CEO(如果小于6个月是0)•年龄:首席执行官在岁•销售:1998年总销售收入(百万美元)•利润的公司:1998年公司利润我(百万美元)•资产:1998年我公司总资产(百万美元)在这个问题上你将产生缺失的数据使用三个机制(MCAR, MAR, NMAR)。接下来，您将应用两种不同的缺失数据方法(完整案例分析和变量均值imputation)，并计算均值、方差和相关矩阵。此外，我们对工资作为利润的函数感兴趣(工资= a + b·利润)。由于数据集是完整的，并且您自己生成了缺失的数据，因此您将能够将两种缺失数据方法的结果与总体值(从完整数据中获得)进行比较。我们将假设只有薪资、薪酬和年龄这些变量会出现数据缺失。其他三个变量不会有缺失值。请按以下步骤操作:在上述三个变量中模拟25%的无响应;您将获得三个数据集，每个场景一个。考虑如何获得MCAR、MAR和NMAR。2. 对于每个不完整的数据集，计算平均值、方差、相关矩阵和上述回归。3.将结果与完整的数据均值、方差、相关矩阵和回归进行比较。4. 写一份报告(最多2页)，描述你的发现。还要描述您是如何生成MCAR、MAR和NMAR的，以及为什么您生成无响应的方法会导致那种特定的机制。6. 把你写的R程序和你用过的R命令单独列在一页上。使用第2章的材料和讲义来找到carsmiss数据集中缺失观测值的最小二乘估计。从网站上下载它，然后使用命令carsmiss <-read.table("carsmiss.txt"，T,sep="，")进入R。数据集carsmiss有四个…
{"title":"Missing Data in Experiments","authors":"R. Little, D. Rubin","doi":"10.1002/9781119013563.CH2","DOIUrl":"https://doi.org/10.1002/9781119013563.CH2","url":null,"abstract":"Application of simple methods Complete case analysis and variable mean imputation Download from the website, and into R a dataset called ceo.dat. The dataset contains information on 6 variables of fortune 500 companies in 1999. It has 447 cases. Originally, the fortune 500 consists of 500 cases, but 53 cases were deleted due to missing values. The six variables are • salary: 1999 CEO salary plus bonuses (thousand $) • totcomp: 1999 CEO total compensation (thousand $) • tenure: number of years as CEO (is 0 if less than 6 months) • age: age of CEO in years • sales: total 1998 sales revenue of firm i (million $) • profits: 1998 profits for firm i (million $) • assets: total assets of firm i in 1998 (million $) In this problem you will generate missing data yourself using three mechanisms (MCAR, MAR, NMAR). Next, you will apply two different missing data methods (complete case analysis and variable mean imputation), and compute means, variances, and the correlation matrix. Also, we are interested in salary as a function of profits (salary= a + b·profits). Since the data set is complete, and you generate missing data yourself, you will be able to compare the results from the two missing data methods to the population values (obtained from the complete data). We will assume that missing data occurs only the variables salary, totcomp, and age. The other three variables will not have missing values. Do the following: 1. Simulate 25% nonresponse in the three variables described; you will obtain three data sets, one for each scenario. Think about how you can get MCAR, MAR, and NMAR. 2. For each incomplete data set, compute the means, variances, the correlation matrix, and the regression described above. 3. Compare the results with the complete data means, variances, the correlation matrix, and the regression. 4. Write a report (2 page max) in which you describe the findings. Also describe how you generated MCAR, MAR, and NMAR, and why your method of generating nonresponse resulted in that particular mechanism. 6. Include R programs you wrote and R commands you used on a separate page. Use the materials from chapter 2, and the handout to find the least squares estimates for the missing observations in the data set called carsmiss. Download it from the website, and into R using the command carsmiss <-read.table(\"carsmiss.txt\",T,sep=\",\"). The data set carsmiss has four …","PeriodicalId":354428,"journal":{"name":"Statistical Analysis with Missing Data, Third Edition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126340514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：