请教各位大侠关于missing data的问题

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我现在做state space model,假如出现missing data一般用kalman filter可以自动跳过,用估计值取代.

但现在遇到的问题是,当missing data出现在the start of the sample的时候,没办法估计出模型的order.

请问各位大侠,一般这种情况该怎么解决?

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关键词：missing SSIN 各位大侠 Miss Data 大侠 Data missing

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[此贴子已经被作者于2006-4-25 4:12:32编辑过]

Multiple imputation of missing data: an implementation of van Buuren's MICE, and more

Patrick Royston (

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patrick.royston@ctu.mrc.ac.uk)
Additional contact information
Patrick Royston: MRC Clinical Trials Unit, London

United Kingdom Stata Users' Group Meetings 2004 from Stata Users Group

Abstract: Following the seminal publications of Rubin starting about 30 years ago, statisticians have become increasingly aware of the inadequacy of `complete case' analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates; and a complete-case analysis may omit as many as half of the available cases. `Hotdeck' imputation was implemented in Stata by Mander and Clayton (1999). However, this technique may perform poorly in the common case when many rows of data have at least one missing value. In this talk, I will describe an implementation for Stata of the `MICE' method of multiple multivariate imputation described by van Buuren et al. (1999) (see also www.multiple-imputation.com). MICE stands for Multivariate Imputation by Chained Equations. The basic idea of data analysis with multiple imputation is to create a small number (e.g. 3-5) copies of the data, each of which has the missing values suitably imputed. Then, each complete dataset is analysed independently. Estimates of parameters of interest are averaged across the copies to give a single estimate. Standard errors are computed according to the `Rubin rules' (Rubin 1987), devised to allow for the between- and within-imputation components of variation in the parameter estimates. In the talk, I will present briefly five ado-files. mvis creates multiple multivariate imputations. uvis imputes missing values for a single variable as a function of several covariates, each with complete data. micombine fits a wide variety of regression models to a multiply imputed dataset, combining the estimates using Rubin's rules. micombine supports survival analysis models (stcox and streg), categorical data models, generalised linear models, and more. Finally, misplit and mijoin are utilities to inter-convert datasets created by mvis and by Carlin et al. (2003)'s miset routine. The use of the routines will be illustrated by example.

Date: 2004-06-30

[此贴子已经被作者于2006-4-25 4:39:10编辑过]

Stata: ICE: Imputation with Chained Equations: ( user supplied ado to Stata)

This can be found by running typing findit ice in the Stata command window. The procedure can then be installed. This is an upgrade to the Stata program MVIS which is an implementation for Stata of the program MICE ( originally implemented in R). Provides single and multiple imputations using a model based approach based on chained regression equations. The user can chose a regression model based on the level of measurement of the variables ( regression, logit, ordered logit, multinomial regression…). A supplied program MICOMBINE allows rolling up the estimates from multiple imputed data sets for many types of regression- based models.

See

Royston, Patrick. 2004. Multiple imputation of missing values. The Stata Journal. 4: 227- 241.

Royston, Patrick. 2005. Multiple imputation of missing values: Update. The Stata Journal. 5: 1- 14.

[此贴子已经被作者于2006-4-25 4:32:13编辑过]

多谢各位

都是比较经典的，尤其前几部 5# Nicolle