On a Statistical Framework for Estimation from Random Set Observations

Using the theory of random closed sets, we extend the statistical framework introduced by Schreiber⁽¹¹⁾ for inference based on set-valued observations from the case of finite sample spaces to compact metric spaces with continuous distributions.     

Detecting Clusters and Nonlinearity in Three-Dimensional Dynamic Graphs

Three-dimensional dynamic scatterplots can reveal certain features of data that cannot be apprehended in marginal two-dimensional displays. Using graduate students as subjects, we sought to establish whether the detection of clusters and nonlinearity in 3-D plots varies by easily characterized properties of the data and the design of the display. We found that the probability of detection of clusters increased smoothly with cluster separation, and that, at a fixed level of separation, “diagonally” displaced clusters were easier to detect than “horizontally” displaced clusters. Cluster detection appeared to be affected to a smaller extent by the design of the display. Three further experiments addressed the detection of nonlinearity in 3-D dynamic scatterplots. Most subjects were able to respond in a reasonable manner to properties of the data, so that the probability of detection of nonlinearity increased with its level, particularly when the signal was strong. As in the experiment on cluster detection, subjects' performance was also affected, though to a lesser extent, by characteristics of the displays; for example, spinning the display horizontally in the regression plane was particularly effective. We discuss the implications of these results for the design of statistical software incorporating dynamic 3-D scatterplots.     

Extensible Statistical Software: On a Voyage to Oberon

Abstract

Recent changes in software technology have opened new possibilities for statistical computing. Conditions for creating efficient and reliable extensible systems have been largely improved by programming languages and systems that provide dynamic loading and type-safety across module boundaries, even at run time. We introduce Voyager, an extensible data analysis system based on Oberon, which tries to exploit some of these possibilities.     

A framework for statistical software development, maintenance, and publishing within an open-access business model

There are several fundamental problems with statistical software development in the academic community. In addition, the development and dissemination of academic software will become increasingly difficult due to a variety of reasons. To solve these problems, a new framework for statistical software development, maintenance, and publishing is proposed: it is based on the paradigm that academic and commercial software should be both cost-effectively created, maintained and published with Marketing Principles in mind. The framework has been seamlessly integrated into a highly successful website () that operates as a provider of free web-based statistical software. Finally it is explained how the R framework provides a platform for the development of a true compendium publishing system.     

R: A Language for Data Analysis and Graphics

Abstract

In this article we discuss our experience designing and implementing a statistical computing language. In developing this new language, we sought to combine what we felt were useful features from two existing computer languages. We feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scoping.     

论文与作者数量的统计分析

对《统计研究》从1984年到2008年的论文和作者的数量进行了统计分析,在显著性水平为0．01时,给出了《统计研究》的论文和作者数量服从Lotka分布的结论,并对这个结果进行了分析。     

多元分析方法在发电技术经济中的应用

本文综合运用因子分析 ,聚类分析等多元统计分析的方法 ,对我国不同地区发电技术水平进行综合分析与评价 ,并划分等级 .     

非均匀随机数产生

本文详细介绍了产生非均匀随机数的一般方法,常用连续分布(正态分布,Gamma分布,Beta分布,χ2分布和F分布)的抽样法及利用R软件产生随机数的方法。     

Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This article provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM). Supplementary materials for this article are available online.