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  • Kim, I.H.; Kang, M.S.; Hines, Robert H.; Hancock, Joe D. (Kansas State University. Agricultural Experiment Station and Cooperative Extension Service, 1994)
    Eight crossbred barrows (initial body wt of 90 lb and 180 lb for four growing and four finishing pigs, respectively) were fitted with T-cannulas at the distal ileum and used in 36-d metabolism experiments (4 x 4 Latin squares) ...
  • Kim, I.H.; Gugle, Terry L.; Hines, Robert H.; Hancock, Joe D. (Kansas State University. Agricultural Experiment Station and Cooperative Extension Service, 1994)
    Ninety nursery pigs were used in two metabolism experiments to determine the effects of roasting and extruding on the nutritional value of Williams 82 soybeans with (+K) and without (-K) gene expression for the Kunitz ...
  • Colaw, Janet Karen (Kansas State University, 1969)
  • Mulanda, Brian Wise (Kansas State University, 2008)
    The best mode of communication for a team of mobile robots deployed to cooperatively perform a particular task is through exchange of messages. To facilitate such exchange, a communication network is required. When successful ...
  • Meng, Li (Kansas State University, 2014)
    In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion ...
  • Yang, Li (Kansas State University, 2014)
    Mixtures of factor analyzers have been popularly used to cluster the high dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. ...
  • Bai, Xiuqin; Yao, Weixin; Boyer, John E. (2012)
    The existing methods for tting mixture regression models assume a normal dis- tribution for error and then estimate the regression parameters by the maximum likelihood estimate (MLE). In this article, we demonstrate ...
  • Bai, Xue (Kansas State University, 2012)
    In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value ...
  • Liu, Yantong (Kansas State University, 2013)
    A robust estimation procedure for mixture errors-in-variables linear regression models is proposed in the report by assuming the error terms follow a t-distribution. The estimation procedure is implemented by an EM algorithm ...
  • Yu, Chun (Kansas State University, 2014)
    Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among y values. Even one single atypical value may have a large effect on the parameter ...
  • Song, Weixing; Yao, Weixin; Xing, Yanru (2014)
    A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture ...
  • Xing, Yanru (Kansas State University, 2013)
    A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure ...
  • Zhang, Jingyi (Kansas State University, 2013)
    A robust estimation procedure for parametric regression models is proposed in the paper by assuming the error terms follow a Pearson type VII distribution. The estimation procedure is implemented by an EM algorithm based ...
  • Wei, Yan (Kansas State University, 2012)
    In this report, we propose a robust mixture of regression based on t-distribution by extending the mixture of t-distributions proposed by Peel and McLachlan (2000) to the regression setting. This new mixture of regression ...
  • Yao, Weixin; Wei, Yan; Yu, Chun (2014)
    The traditional estimation of mixture regression models is based on the normal assumption of component errors and thus is sensitive to outliers or heavy-tailed errors. A robust mixture regression model based on the ...
  • BAI, XIUQIN (Kansas State University, 2014)
    This proposal contains two projects that are related to robust mixture models. In the rst project, we propose a new robust mixture of regression models (Bai et al., 2012). The existing methods for tting mixture regression ...
  • Bai, Xiuqin (Kansas State University, 2010)
    In the fitting of mixtures of linear regression models, the normal assumption has been traditionally used for the error term and then the regression parameters are estimated by the maximum likelihood estimate (MLE) using ...
  • Yao, Weixin; Wang, Qin (2013)
    Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an ...
  • Hembree, David (Kansas State University, 2012)
    Effect size is a concept that was developed to bridge the gap between practical and statistical significance. In the context of completely randomized one way designs, the setting considered here, inference for effect size ...
  • Devamitta Perera, Muditha Virangika (Kansas State University, 2011)
    The variance of a response in a one-way random effects model can be expressed as the sum of the variability among and within treatment levels. Conventional methods of statistical analysis for these models are based on the ...