• A study of trends in tennis matches

    分类: 统计学 >> 应用统计数学 提交时间: 2024-02-20

    摘要: 本研究旨在通过分析比赛流程,准确预测比赛中的趋势与走向的变化。为了捕捉比赛流程,我们先定义了一个 A 值,并开发了一个决策树模型。此外,我们还建立了一个非线性自回归神经网络来实现预测功能。在模型改进过程中,我们计算了皮尔逊相关系数,以衡量影响程度。结果表明,该模型相对成功地实现了预测功能。Ace数目、双失误和非受迫性失误是关键的影响因素。

  • Modeling of New Energy Vehicles’ Impact on Urban Ecology Focusing on Behavior

    分类: 统计学 >> 应用统计数学 分类: 计算机科学 >> 计算机应用技术 分类: 数学 >> 建模与仿真 分类: 能源科学 >> 能源(综合) 提交时间: 2024-01-01

    摘要: The surging demand for new energy vehicles is propelled by the call to conserve energy, curtail emissions, and enhance the ecological ambience. By conducting behavioral analysis and mining, particular usage patterns of new en#2;ergy vehicles are pinpointed. Regrettably, these models decrease their environ#2;mental shielding efficiency. For instance, overloading the battery, operating with low battery power, and driving at excessive speeds can all detrimentally affect the battery's performance. To assess the impact of such driving behavior on the urban ecology, an environmental computational modeling method has been pro#2;posed to simulate the interaction between new energy vehicles and the environ#2;ment. To extend the time series data of the vehicle's entire life cycle and the eco#2;logical environment within the model sequence data, I utilized the LSTM deep learning method with Bayesian optimizer optimization parameters for longer simulation. The analysis revealed the detrimental effects of poor driving behavior on the environment

  • 数字化背景下思想政治教育成效评价方法探析——以高校辅导员谈心谈话为例

    分类: 法学 >> 法学其他学科 分类: 统计学 >> 应用统计数学 提交时间: 2023-12-04

    摘要: 党的二十大报告指出:深化教育领域综合改革,完善学校管理和教育评价体系。新时代高校思想工作主体、对象、内容、效果呈现了新的特征,面临着思想政治工作高质量发展的挑战。针对思想政治工作的政治性、时代性、系统性、科学性宏大体系,对思想政治工作成效进行科学评价显得尤为重要。实施数字转型,运用数据科学技术和统计综合评价理论对高校思想政治工作成效进行评价,是为这项工作提质增效提出对策建议的一个重要手段。以高校辅导员谈心谈话成效评价为例对思想政治工作成效评价进行探析,是提升思想政治工作研究学术水平的重要实践,有助于运用大数据技术对思想政治工作进行重塑。

  • Understanding principal component analysis

    分类: 统计学 >> 应用统计数学 提交时间: 2023-09-15

    摘要: The principal component analysis (PCA) is a frequently used machine learning method. In this paper, the PCA operation is explained by examples with Python program illustration. A proof of the diagonalizability of real symmetric matrix is also included, which may help to understand the mathematics behind PCA.

  • Solar Term Anomaly in China Stock Market: Evidence from Shanghai Index

    分类: 统计学 >> 经济统计学 分类: 统计学 >> 应用统计数学 提交时间: 2023-02-10

    摘要: 本文研究了在中国股票市场的节气效应(异象)以作为对现有日历效应文献的补充。基于回归模型,本文从多个维度证实了上证指数存在的节气效应:节气组内研究、全样本均值研究和、全样本波动率研究以及节气交替效应。如小寒、立春和雨水等节气被证实能够带来显著正或负的收益率,谷雨、芒种及大暑节气会显著地带来高波动率。这些结果是可信的并且在EBA检验和多种误差分别假设下也是稳健的。本文的结果为读者提供了一个全新的角度来理解在中国传统文化影响下的日历效应的表现。节气效应是通过影响投资者情绪进而影响市场的。这对文化红利假说和中国文化对其他亚洲市场的潜在影响都是一个有力的依据。

  • Application of generalised linear regression GARMA in tourism area

    分类: 统计学 >> 应用统计数学 提交时间: 2021-01-30

    摘要: From a modelling perspective, our first contribution is to propose generalised linear regression GARMA (GLRGARMA) model and generalised linear regression SARMA (GLRSARMA) model with a innovative function of explanatory variables in order to extend GLGARMA to incorporate relevant information for model fitting and forecast in tourism area. Besides, the generalised Poisson (GP) distribution is adopted to accommodate over- equal- and under-dispersion for certain tourism data. Moreover, the performance of GLRGARMA model and GLRSARMA model with their nested sub-models are compared and evaluated using several well-known selection criteria. Our second contribution is to investigate the behaviour of tourism data. The pattern of long memory is examined. The analysis of Hurst exponent, ACF plot and periodogram plot shows that Gegenbauer long memory features are presented in tourism data. Furthermore, the distinct characteristics between Gegenbauer long memory and seasonality are demonstrated to reveal the that the GLRGARMA model is more suitable for modelling tourism data. Our third contribution is to derive a Bayesian approach via the efficient and user-friendly Rstan package in estimating our proposed models. For ML approach, the likelihood function is untractable because of involving very high dimensional integrals. Several monitors of convergence of posterior samples are discussed, such as the number of effective sample and bR estimate. The criteria for modelling performance are also derived.

  • Strengthened change point detection model for weak mean difference data

    分类: 统计学 >> 应用统计数学 提交时间: 2019-04-22

    摘要: Objective: The lifetime difference in adjacent parallel structure components becomes small as the number of components belonging to the same parallel structure increases. To infer the system structure, we must clarify the components that belong to the same parallel structure. Methods: A strengthened change point detection model (SCPDM) for weak mean difference data (WMDD) is established, which usually indicates that, as affected by a large variance, the mean difference in two subsignals for one data sequence becomes nonsignificant. For repeatedly retrievable WMDD, we performed two enhanced operations that doubled the mean difference by using the variance information and analyzed the asymptotic properties of the enhanced data. Then, we proposed an SCPDM based on the asymptotic results.Results: Finally, we compared the SCPDM with two other main change point detection models and verified that the SCPDM is superior to other models using WMDD change point detection by the simulation method.Limitations: This paper also have several limitations. First, we only discussed that are independent with normal distribution and single change point. Second, the reason why the relationship between and has an important influence on the accuracy of change point detection is not discussed in depth. We only defined the ratio boundary of WMDD by experience and simulation. Conclusions: Traditional change point detection models may become insensitive or ineffective for WMDD. We gave some asymptotic analysis and established a enhanced change point detection model (SCPDM) based on the asymptotic results. Compared with the traditional method, SCPDM can effectively detect the change point.