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软件简介： <trans data-src="Discriminant analysis for longitudinal data with application in medical diagnostics. ">纵向数据判别分析及其在医学诊断中的应用。</trans><trans data-src="Classification methods for longitudinal data bear the potential to identify classifiers that are superior to those based on cross-sectional data. ">纵向数据的分类方法具有识别优于基于横截面数据的分类器的潜力。</trans><trans data-src="Recently, the univariate longitudinal quadratic discriminant analysis (longQDA) was proposed for such purposes. ">最近，一元纵向二次判别分析（longQDA）被提出。</trans><trans data-src="Its key idea is to use marginal means and covariance matrices of linear mixed models as group-specific plug-in estimators for the discriminant rule. ">它的核心思想是利用线性混合模型的边际均值和协方差矩阵作为判别规则的组特定插件估计量。</trans><trans data-src="This dissertation investigates some of the unaddressed issues as model selection and several multivariate extensions. ">本文研究了一些尚未解决的问题，如模型选择和多变量扩展。</trans><trans data-src="A complementary software implementation in R is presented which fulfills state-of-the-art design and user requirements. ">提出了一个用R语言实现的补充软件，它满足了最新的设计和用户需求。</trans><trans data-src="Longitudinal biomarker data from diagnostic studies that are assessed for their potential to classify patients as therapy-resistant or not serve as motivating applications. ">来自诊断研究的纵向生物标志物数据，评估其将患者分类为抗治疗或不作为激励应用的潜力。</trans><trans data-src="First, we compare two model selection criteria for determining the most appropriate univariate linear mixed model structures for each group and quantify the corresponding bias of an incorrect decision. ">首先，我们比较了两种模型选择准则，以确定最合适的单变量线性混合模型结构，并量化了错误决策的相应偏差。</trans><trans data-src="The first criterion selects the model structure that yields the best classification performance. ">第一个准则选择产生最佳分类性能的模型结构。</trans><trans data-src="The second selects the model with the minimal Bayesian information criterion and performs better in our simulation study. ">第二种方法选择了贝叶斯信息最小准则的模型，并在仿真研究中取得了较好的效果。</trans><trans data-src="The bias of an incorrect decision turns out to be higher for longer data profiles and more complex longitudinal models with random effects. ">对于较长的数据剖面和具有随机效应的更复杂的纵向模型，错误决策的偏差更大。</trans><trans data-src="Subsequently, we present multivariate extensions of long QDA.Two multivariate mixed model classes with a parsimonious parametrization are proposed: multivariate random effects models and covariance pattern models with a Kronecker product structure. ">随后，我们给出了长QDA的多变量扩展，提出了两类参数化简洁的多元混合模型类：具有Kronecker积结构的多元随机效应模型和协方差模式模型。</trans><trans data-src="With a special set-up of the data, estimation algorithms implemented for the univariate case are used for the first model class. ">在数据的特殊设置下，第一个模型类使用单变量情况下实现的估计算法。</trans><trans data-src="The restricted maximum likelihood estimation of Kronecker product models is accomplished by a numerical constraint optimization algorithm. ">Kronecker积模型的限制极大似然估计是通过数值约束优化算法实现的。</trans><trans data-src="Finally, we introduce the R package long QDA for executing quadratic discriminant analysis with longitudinal data. ">最后，我们介绍了用于纵向数据二次判别分析的R包long QDA。</trans><trans data-src="Beyond the statistical methodology presented in this dissertation, the entire process of data analysis up to the reporting of the results is supported. ">除了本文提出的统计方法外，还支持从数据分析到结果报告的整个过程。</trans><trans data-src="The software implementation follows the modern object-orientated concept with S4 classes and fulfills conceptual requirements such as a user-friendly handling, a good run-time performance and easy extensibility. ">软件实现遵循了现代面向对象的概念和S4类，满足了用户友好的处理、良好的运行时性能和易扩展性等概念性要求。</trans><trans data-src="The latter quality criterion is demonstrated for two features: the functionalities for multivariate data settings and its use in simulation studies.">后一个质量标准被证明有两个特点：多变量数据设置的功能及其在模拟研究中的应用。</trans>

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评论： <trans data-src="Update software longQDA: http://swmath.org/software/20397">更新软件longQDA:http://swmath.org/software/20397</trans>

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