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Report of the Co-Editors – 2003

Cullis, Brian; Commenges, Daniel; Kenward, Mike; Lin, Xihong - In: Biometrics 60 (2004) 1, pp. i-i

Persistent link: https://www.econbiz.de/10010946798

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation by Ming T. Tan, Guo‐Liang Tian, Kai Wang Ng

Kenward, Mike - In: International Statistical Review 78 (2010) 3, pp. 479-479

Persistent link: https://www.econbiz.de/10009216071

Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend

Smith, Alison; Cullis, Brian; Thompson, Robin - In: Biometrics 57 (2001) 4, pp. 1138-1147

Persistent link: https://www.econbiz.de/10010947451

A Sparse Implementation of the Average Information Algorithm for Factor Analytic and Reduced Rank Variance Models

Thompson, Robin; Cullis, Brian; Smith, Alison; Gilmour, … - In: Australian & New Zealand Journal of Statistics 45 (2003) 4, pp. 445-459

Persistent link: https://www.econbiz.de/10005313005

An efficient computing strategy for prediction in mixed linear models

Gilmour, Arthur; Cullis, Brian; Welham, Sue; Gogel, Beverley - In: Computational Statistics & Data Analysis 44 (2004) 4, pp. 571-586

Persistent link: https://www.econbiz.de/10005172554

Design aspects of calibration studies in nutrition, with analysis of missing data in linear measurement error models

Carroll, Raymond J.; Freedman, Laurence; Pee, David - 1997

Motivated by an example in nutritional epidemiology, we investigate some design and analysis aspects of linear measurement error models with missing surrogate data. The specific problem investigated consists of an initial large sample in which the response (a food frequency questionnaire, FFQ)...

Persistent link: https://www.econbiz.de/10010310753