Comparing the accuracy of several network-based COVID-19 prediction algorithms

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.     

THE "VEREENIGING VOOR DE STATISTIEK", 1857–1892

This paper deals with the history of the nineteenth century Dutch statistical society: "Vereeniging voor de Statistiek", founded in 1857. In the second half of the nineteenth century the society flourished and had a considerable amount of members. It played a very active role for the establishment of a central official statistical bureau: "Centraal Bureau voor de Statistiek". These activities ended with success in 1892. Then the society changed its name and did no longer engage itself with statistics. Members of the statistical society thought of statistics as facts about society, mostly numerical, but not always. They hoped to discover the laws which ruled society, by means of statistics. In that way the social sciences would improve.     

Modeling Clustered Ordered Categorical Data: A Survey

This article surveys various strategies for modeling ordered categorical (ordinal) response variables when the data have some type of clustering, extending a similar survey for binary data by Pendergast, Gange, Newton, Lindstrom, Palta & Fisher (1996). An important special case is when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. A much greater variety of models and fitting methods are available than when a similar survey for repeated ordinal response data was prepared a decade ago (Agresti, 1989). The primary emphasis of the review is on two classes of models, marginal models for which effects are averaged over all clusters at particular levels of predictors, and cluster-specific models for which effects apply at the cluster level. We present the two types of models in the ordinal context, review the literature for each, and discuss connections between them. Then, we summarize some alternative modeling approaches and ways of estimating parameters, including a Bayesian approach. We also discuss applications and areas likely to be popular for future research, such as ways of handling missing data and ways of modeling agreement and evaluating the accuracy of diagnostic tests. Finally, we review the current availability of software for using the methods discussed in this article.     

统计技术在计量标准考核中的应用

阐述了计量标准考核的现状及存在问题，提出了在计量标准考核中运用数理统计技术的具体方法、步骤，即通过核查标准建立测量过程统计控制，进而实现实验室的运行控制。     

On randomization, modeling and experimental design; a new example in an old discussion

Fisher and "Student" quarreled in the early days of statistics about the design of experiments, meant to measure the difference in yield between to breeds of corn. This discussion comes down to randomization versus model building. More than half a century has passed since, but the different views remain. In this paper the discussion is put in terms of artificial randomization and natural randomization, the latter being what remains after appropriate modeling. Also the Bayesian position is discussed. An example in terms of the old corn-breeding discussion is given, showing that a simple robust model may lead to inference and experimental design that outperforms the inference from randomized experiments by far. Finally similar possibilities are suggested in statistical auditing.     

Estimation methods for stochastic volatility models: a survey

Abstract.  Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.     

Reciprocity,Multiplexity, and Exchange: Measures

We introduce measures and statistical tests for multiplexity and exchange that are modeled on similar ideas developed for reciprocity quite early in the history of social network research. As properties of a multi-relation network, multiplexity, and exchange have almost as ancient a history as reciprocity, but have not been as intensively investigated from a methodological perspective. Multiplexity refers to the extent to which two ties, for example, advice and friendship, coincide over population; that is, do respondents name the same people as friends as the persons they nominate as individuals from who they seek advice. Exchange refers to the extent to which a tie of one type directed from person i to person j is returned by a tie of another type from j to i. We conceive of the current paper as the first part of a two-part paper, wherein the second part explores specific theoretical models for multiplexity and exchange.     

昌吉电力物资库存数量的确定方法研究

从研究电网公司所处的电力行业特点入手,探讨了昌吉电网公司物资的特点。主要基建物资采取零库存管理的方式,通过库存周转率的控制,使其保持在一个持续周转的过程,尽量减少搬运以及库存资源的浪费;常设库存分类管理,利用存储论及数理统计原理,确定最优库存量,为的是利用有限的仓库资源发挥最大效益的目的。     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

机场净空区超高障碍物风险性分析

针对机场净空区超高障碍物较多的现象,对飞机飞行航迹进行了测试,提出了净空安全系数的概念,结合实际测试数据,应用统计分析原理,得出了净空安全系数的概率分布规律.以安全系数的分布规律为基础,对不同超高障碍物的风险性进行了分析,并提出了按照不同安全系数划分障碍物限制高度的思路,最后给出了不同类型机场净空安全系数的建议值,对各类机场净空管理和障碍物限制面的确定具有重要参考价值.