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      by 
      Mendenhall, William
      Format: 
      Excerpt: 
      A second course in statistics : regression analysis / Mendenhall, William
      by 
      Marchese, Scott, author.
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      Excerpt: 
      Semiparametric Regression Methods for Mixed Type Data Analysis / Marchese, Scott, author.
      by 
      Broström, Göran.
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      Regression analysis.
      by 
      Addo, Evans Dapaa, author.
      Format: 
      Excerpt: 
      linear regression; linear regression, non Bayesian) in the MICE package in the statistical software, R
      by 
      Wang, Yiqing, author.
      Format: 
      Excerpt: 
      identifiability conditions for this proposed model. We also consider competing risks regression framework and
      by 
      Stanley, Sarah, author.
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      Excerpt: 
      models the placement values through beta regression. We compare the beta regression method to the
      by 
      Wilcox, Rand R.
      Format: 
      Excerpt: 
      for applying cutting-edge techniques * Covers many contemporary ANOVA (analysis of variance) and
      by 
      Natarajan, Balasubramaniam, author.
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      Excerpt: 
      estimator based on Beta kernels. For both these regression estimators, a comprehensive analysis of its large
      by 
      Li, Xia, author.
      Format: 
      Excerpt: 
      due to variance inflation. A preliminary analysis on simulated samples drawn from a population of real
      by 
      Zhu, Ziwei, author.
      Format: 
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      result. We applied this approach to low-dimensional regression, high-dimensional sparse regression and
      by 
      Dong, Junyi, author.
      Format: 
      Excerpt: 
      Regression analysis studies the relationships among the response variable and the predictors. Some
      by 
      Sage, Andrew John, author. (orcid)0000-0003-4656-9748
      Format: 
      Excerpt: 
      regression. Adapted from a popular approach used in polynomial regression, our method uses residual analysis
      by 
      Acharjee, Mithun Kumar, author.
      Format: 
      Excerpt: 
      Analysis (LCA) to estimate these two components and determine how much of the variation in estimates can be
      by 
      Nguyen, Son, author.
      Format: 
      Excerpt: 
      Dimension reduction for regression analysis has been one of the most popular topics in the past two
      by 
      Rahman, Pk. Md. Motiur. author.
      Format: 
      Excerpt: 
      . Specifically, findings from logistic regression analysis, polychoric principal component analysis, kernel
      by 
      Cui, Yifan, author.
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      Excerpt: 
      We first carry out a comprehensive analysis of survival random forest and tree models and show the
      by 
      Wang, Zhenchuan, author.
      Format: 
      Excerpt: 
      simulation studies, we compared the performance of AWRR with canonical correlation analysis (CCA), Single
      by 
      Ondeck, Nathaniel Thomas, author.
      Format: 
      Excerpt: 
      Objective 2, when utilizing complete case analysis, only 4,311 patients of the 11,999 patients in the cohort
      by 
      Sen, Pranab Kumar, 1937- editor.
      Format: 
      Excerpt: 
      procedures in biostatistics / Y. Hochberg and P. Wastfall -- Analysis of longitudinal data / J.M. Singer and