In the Chinese Cultural Revolution, millions of middle school and high school graduates, called the zhiqing or Educated Youth, were sent up to the mountains and down to the countryside to receive reeducation from the poor peasants. With deep conviction that they would play an important role in the transformation of rural China, the zhiqing became field hands, never realizing that reeducation was both a physical and psychological challenge. This collection of poetry is the representation of those reeducation years in the fields. Half a century has passed, but memories remain fresh, each a page of suffering, cheering, or dreaming to turn.
During China’s Cultural Revolution, millions of middle school and high school graduates, called zhiqing, were sent to the countryside to receive reeducation from peasants. They dug the earth daily, with deep conviction that they would play an important role in the transformation of rural China. Jianqing Zheng’s rusticated years were central to his poetic imagination in this collection. “The poems in this collection reveal a complex narrative of China’s Cultural Revolution and its Sent-Down-Youth. While each poem stands alone—graced with stunning descriptions (“At fish-belly dawn”), images (“hands fluttering / like butterflies on cotton fluffs”), and all too human struggles (“I am tired of being tired; / of being told what to do”)—together they tell of a larger history.”—Rita Costello
In the Chinese Cultural Revolution, millions of middle school and high school graduates, called the zhiqing or Educated Youth, were sent up to the mountains and down to the countryside to receive reeducation from the poor peasants. With deep conviction that they would play an important role in the transformation of rural China, the zhiqing became field hands, never realizing that reeducation was both a physical and psychological challenge. This collection of poetry is the representation of those reeducation years in the fields. Half a century has passed, but memories remain fresh, each a page of suffering, cheering, or dreaming to turn.
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
This book presents an overview of recent developments in biostatistics and bioinformatics. Written by active researchers in these emerging areas, it is intended to give graduate students and new researchers an idea of where the frontiers of biostatistics and bioinformatics are as well as a forum to learn common techniques in use, so that they can advance the fields via developing new techniques and new results. Extensive references are provided so that researchers can follow the threads to learn more comprehensively what the literature is and to conduct their own research. In particulars, the book covers three important and rapidly advancing topics in biostatistics: analysis of survival and longitudinal data, statistical methods for epidemiology, and bioinformatics.
This book furthers new and exciting developments in experimental designs, multivariate analysis, biostatistics, model selection and related subjects. It features articles contributed by many prominent and active figures in their fields. These articles cover a wide array of important issues in modern statistical theory, methods and their applications. Distinctive features of the collections of articles are their coherence and advance in knowledge discoveries.
During the last two decades, many areas of statistical inference have experienced phenomenal growth. This book presents a timely analysis and overview of some of these new developments and a contemporary outlook on the various frontiers of statistics.Eminent leaders in the field have contributed 16 review articles and 6 research articles covering areas including semi-parametric models, data analytical nonparametric methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model diagnostics, robust multivariate analysis, single index models and wavelets.This volume is dedicated to Prof. Peter J Bickel in honor of his 65th birthday. The first article of this volume summarizes some of Prof. Bickel''s distinguished contributions.
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