#### Filter Results:

- Full text PDF available (3)

#### Publication Year

2009

2017

- This year (1)
- Last 5 years (5)
- Last 10 years (8)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Zuber D. Mulla, Byungtae Seo, Ramaswami Kalamegham, Bahij Nuwayhid
- Annals of epidemiology
- 2009

PURPOSE
To present multiple imputation (MI) as an appropriate method to address missing values for a laboratory parameter (serum albumin) in an epidemiologic study.
METHODS
A data set of patients who were hospitalized for invasive group A streptococcal infections was accessed. Age was the exposure of interest. The outcome was hospital mortality. Several… (More)

- Byungtae Seo, Bruce G. Lindsay
- Computational Statistics & Data Analysis
- 2010

In some models, both parametric and not, maximum likelihood estimation fails to be consistent. We investigate why the maximum likelihood method breaks down with some examples and notice the paradox that, in those same models, maximum likelihood estimation would have been consistent if the data had been measured with error. With this motivation we define… (More)

- Byungtae Seo
- Computational Statistics & Data Analysis
- 2017

- Byungtae Seo, Daeyoung Kim
- Computational Statistics & Data Analysis
- 2012

Finite mixtures of normal distributions are attractive in identifying the underlying group structure in the data. However, it is a challenging task to do statistical inference in normal mixture models using the method of maximum likelihood, due to the unbounded likelihood and the existence of multiple roots to the likelihood equation including a so-called… (More)

- Sijia Xiang, Weixin Yao, Byungtae Seo
- Computational Statistics & Data Analysis
- 2016

- Daeyoung Kim, Byungtae Seo
- J. Multivariate Analysis
- 2014

- Hyekyung Jung, Joseph L. Schafer, Byungtae Seo
- Computational Statistics & Data Analysis
- 2011

A Latent-Class Selection Model for Nonignorably Missing Data Most missing-data procedures assume that the missing values are ignorably missing or missing at random (MAR), which means that the probabilities of response do not depend on unseen quantities. Although this assumption is convenient, it is sometimes questionable. For example, questionnaire items… (More)

- ‹
- 1
- ›