A Starting Point for Navigating the World of Daily Fantasy Basketball

Fantasy sports, particularly the daily variety in which new lineups are selected each day, are a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, have been valued as high as $2 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. Permutation based and K-nearest neighbors approaches are compared in terms of the identification of “successful” teams—those who would be competitive more often than not based on historical data. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015–2016 season. Our results show an expected profit of approximately $9,000 on an initial $500 investment using the K-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.     

Cytometry inference through adaptive atomic deconvolution

In this paper, we consider a statistical estimation problem known as atomic deconvolution. Introduced in reliability, this model has a direct application when considering biological data produced by flow cytometers. From a statistical point of view, we aim at inferring the percentage of cells expressing the selected molecule and the probability distribution function associated with its fluorescence emission. We propose here an adaptive estimation procedure based on a previous deconvolution procedure introduced by Es, Gugushvili, and Spreij [(2008), ‘Deconvolution for an atomic distribution’, Electronic Journal of Statistics, 2, 265–297] and Gugushvili, Es, and Spreij [(2011), ‘Deconvolution for an atomic distribution: rates of convergence’, Journal of Nonparametric Statistics, 23, 1003–1029]. For both estimating the mixing parameter and the mixing density automatically, we use the Lepskii method based on the optimal choice of a bandwidth using a bias-variance decomposition. We then derive some convergence rates that are shown to be minimax optimal (up to some log terms) in Sobolev classes. Finally, we apply our algorithm on the simulated and real biological data.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.     

基于Lasso的外商直接投资影响因素研究

在影响外商直接投资的众多因素中,市场规模、基础设施、关税、贸易开放度以及劳动生产率是影响外商直接投资的5个主要因素,其中市场规模的影响远远大于其他因素的影响,同时,把Lasso方法、最小二乘法及逐步回归法进行了比较,从所得结果可以看出,Lasso方法在变量选择方面优于另外两种方法。     

一类新的自适应非单调谱投影梯度法

给出了求解凸约束优化的一类新的自适应非单调谱投影梯度法.通过引入具有自适应性的权重参数,使算法在迭代过程中能自动调节非单调策略. 在适当条件下证明了算法的收敛性.数值试验结果表明,该算法在一定程度上能减少在线搜索过程中对非单调参数M的依赖.     

企业战略与环境的适应性:一个复杂适应系统的视角

企业战略的重要特性之一是它对环境的适应性,它强调企业组织能够运用自己的资源去适应组织外部环境和内在条件的变化。企业环境是不确定的,面对不同层次的不确定性环境,企业的适应性战略主要包括外包战略、联盟战略、柔性战略。     

一种实用自适应PID控制器

本文介绍了一种实用自适应PID控制器,它是以MCS-51系列单片微机为核心,采用PID控制规律结合参数辨识的控制算法,实现了PID参数完全在线自动整定。该控制器适用于被控制对象未知或慢时变的场合,既可替代常规PID控制器,也可作为分布式系统的低级智能控制器。     

生态系统理论视域下研究生在学术梯队中的适应性发展

为促进研究生在学术梯队中的心理适应和成长,进而促进高校学术梯队生态系统的持续发展,研究借鉴生态系统理论,采用质性研究的方法,对某高校学术梯队部分成员进行了深度访谈。研究发现,在团队合作良好的学术梯队中,研究生更倾向于通过融入和合作的方式谋求自我成长和梯队发展。研究将研究生的发展置于学术梯队生态系统之中,建构了研究生成员、导师成员及梯队系统共同促进和发展的内部作用模型,并着重指出研究生对于学术梯队维持和发展模式的理解有利于促进其融入与合作。