大数据时代:多元与高维数据分析整合与应用

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《多元与高维数据分析》是一本针对大数据时代挑战的现代教材,它将传统的多变量方法与机器学习和工程领域的当代技术融合在一起,提供了一种连贯的处理框架。这本书的核心内容围绕理论、数据、计算和最新研究成果展开。 首先,作者构建了一个严谨的理论框架,包括形式定义、定理和证明,这些内容为各种分析方法设定了明确的安全操作区域。用户可以根据数据特性判断是否处于这个区域,确保方法的有效性和可靠性。书中的实例涵盖了广泛的应用场景,从经典的少量变量数据到医学、生物学、市场营销和金融等领域的真实数据,再到生物信息学的高维度数据、蛋白质组学的功能数据以及模拟数据,展示了不同方法在实际问题中的优势和局限性。 特别地,对于高维低样本量的数据,作者给予了特别的关注。书中反复剖析几个数据集,目的是为了比较和评估不同方法在处理这类复杂数据时的性能。丰富的彩色图表、算法示例、MATLAB代码和配套的问题集,使得读者能够通过实践深化理解,并能在实际工作中灵活应用。 该书的目标读者群体是统计学的研究生和在数据密集型学科(如生物信息学、金融工程等)进行研究的人员。通过这本教材,他们不仅可以掌握多元和高维数据分析的最新技术和理论,还能培养解决实际问题的能力,为在大数据时代的职业发展打下坚实的基础。Inge Koch教授作为澳大利亚阿德莱德大学的统计学副教授,她的专业知识和经验为本书的质量提供了有力保障。《多元与高维数据分析》是一本实用且深入的教材,对于理解和应对大数据时代的挑战具有重要的参考价值。
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This book is about data in many – and sometimes very many – variables and about analysing such data. The book attempts to integrate classical multivariate methods with contemporary methods suitable for high-dimensional data and to present them in a coherent and transparent framework. Writing about ideas that emerged more than a hundred years ago and that have become increasingly relevant again in the last few decades is exciting and challenging.With hindsight, we can reflect on the achievements of those who paved the way, whose methods we apply to ever bigger and more complex data and who will continue to influence our ideas and guide our research. Renewed interest in the classical methods and their extension has led to analyses that give new insight into data and apply to bigger and more complex problems. There are two players in this book: Theory and Data. Theory advertises its wares to lure Data into revealing its secrets, but Data has its own ideas. Theory wants to provide elegant solutions which answer many but not all of Data’s demands, but these lead Data to pose new challenges to Theory. Statistics thrives on interactions between theory and data, and we develop better theory when we ‘listen’ to data. Statisticians often work with experts in other fields and analyse data from many different areas. We, the statisticians, need and benefit from the expertise of our colleagues in the analysis of their data and interpretation of the results of our analysis. At times, existing methods are not adequate, and new methods need to be developed. This book attempts to combine theoretical ideas and advances with their application to data, in particular, to interesting and real data. I do not shy away from stating theorems as they are an integral part of the ideas and methods. Theorems are important because they summarise what we know and the conditions under which we know it. They tell us when methods may work with particular data; the hypotheses may not always be satisfied exactly, but a method may work nevertheless. The precise details do matter sometimes, and theorems capture this information in a concise way. Yet a balance between theoretical ideas and data analysis is vital. An important aspect of any data analysis is its interpretation, and one might ask questions like: What does the analysis tell us about the data?What new insights have we gained from a particular analysis? How suitable is my method formy data?What are the limitations of a particular method, and what other methods would produce more appropriate analyses? In my attempts to answer such questions, I endeavour to be objective and emphasise the strengths and weaknesses of different approaches.