Functional data analysis and nonlinear regression models: an information quality perspective

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
R. Kenett, C. Gotwalt
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引用次数: 1

Abstract

Abstract Data from measurements over time can be analyzed in different ways. In this article, we compare functional data analysis and nonlinear regression models using, among others, eight information quality dimensions. We present two case studies. The first case study introduces functional data analysis and nonlinear regression models in analyzing dissolution profiles of drug tablets where profiles of tablets under test are compared to reference tablets. A second case study involves statistically designed mixture experiments used in optimization tablet formulation. Python and JMP features are used to demonstrate the methods used in the two case studies.
功能数据分析和非线性回归模型:信息质量视角
摘要随时间变化的测量数据可以通过不同的方式进行分析。在本文中,我们比较了函数数据分析和非线性回归模型,其中使用了八个信息质量维度。我们介绍了两个案例研究。第一个案例研究介绍了功能数据分析和非线性回归模型,用于分析片剂的溶出度分布,其中将受试片剂的分布与对照片剂的分布进行比较。第二个案例研究涉及用于优化片剂配方的统计设计的混合物实验。Python和JMP特性用于演示两个案例研究中使用的方法。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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