Nonparametric and Semiparametric Modelling for Data Analysis

对于许多科学问题，非参数和半参数统计建模有助于更好地理解和分析。这包括曲线估计技术的应用。P.I.探索了结合平滑(非参数)、非平滑(不连续)和参数分量的模型。对于高维数据，例如曲线数据和纵向数据，合理的建模方法包括实现降维的组件。这些通常是模型的参数部分，然后在第二步中将非参数平滑器应用于降维数据。P.I.开发了面向科学问题的模型，这些模型以曲线样本、高维数据或不连续数据的形式产生数据。所使用的技术包括广义线性模型、估计方程、变点分析、随机效应建模和平滑(特别是核和局部多项式平滑器)。所提出的带有不连续部分的模型被用来解决儿童的成长是连续还是不连续的问题。第二个应用是DNA序列的建模，其中基频的不连续使得DNA分割技术成为可能。进一步的应用包括图像分析中的边缘检测和连续记录的金融数据的分割，例如股票市场指数。此外，P.I.还为高维数据的高效分析构建模型。一个例子是营养学中的剂量反应分析，目的是找到最佳的维生素来源。P.I.还提出了数据模型，其中每个实验单位记录了整个函数。这类数据出现在许多领域，特别是生命科学领域，需要创新的统计方法。一个恰当的例子是一大群蜻蜓各自记录的产卵行为的数据。衰老研究中的一个主要问题是生殖和寿命之间的关系。因此，特别有兴趣应用拟议的模型来探讨这一联系。

For many scientific problems, nonparametric and semiparametric statistical modelling can contribute to a better understanding and analysis. This includes the application of curve estimation techniques. The P.I. explores models which combine smooth (nonparametric), non-smooth (discontinuous) and parametric components. For high-dimensional data, such as curve data and longitudinal data, a sensible modelling approach includes components which achieve a dimension reduction. These are typically parametric parts of a model, and nonparametric smoothers are then applied in a second step to the dimension reduced data. The P.I. develops models which are geared towards scientific problems which yield data in the form of samples of curves, high dimensional data or discontinuous data. The techniques used include generalized linear models, estimating equations, change-point analysis, random effects modelling and smoothing (in particular, kernel and local polynomial smoothers).The proposed models with discontinuous parts are used to address the question whether the growth of children proceeds continuously or discontinuously. A second application is the modelling of DNA sequences here discontinuities in base frequencies enable DNA segmentation techniques. Further applications include edge detection in image analysis and the segmentation of continuously recorded financial data such as stock market indices. In addition, the P.I. constructs models for the efficient analysis of high dimensional data. An example is dose-response analysis in nutrition with the aim of finding optimal vitamin sources. The P.I. also proposes models for data where an entire function is recorded per experimental unit. Such data occur in many fields, notably the life sciences, and require innovative statistical approaches. A pertinent example are data on the individually recorded egg-laying behavior for a large cohort of medflies. A major question in aging research is the relation between reproduction and longevity. It is therefore of particular interest to apply the proposed models to explore this connection.