Subjects: Statistics >> Applied Statistical Mathematics submitted time 2024-02-20
Abstract： This study aims to accurately anticipate the shifts in a match by analyzing the flow of play. To achieve this, our model has been enhanced by scrutinizing the factors that impact its forecasting capabilities. To capture the flow of play, an A-value has been defined and a decision tree model has been developed. Additionally, we have built a Nonlinear Autoregressive Neural Network to fulfill the forecasting function. During the model improvement process, we calculated the Pearson correlation coefficient to gauge the extent of impact. The results indicate that the model performs its predictive function with relative success and that aces, double faults, and unforced errors are the key influencing factors.
Peer Review Status:
Awaiting Review
Subjects: Statistics >> Applied Statistical Mathematics Subjects: Computer Science >> Computer Application Technology Subjects: Mathematics >> Modeling and Simulation Subjects: Energy Science >> Energy Science （General） submitted time 2024-01-01
Abstract： 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
Peer Review Status:Awaiting Review
Subjects: Law >> Other disciplines of law Subjects: Statistics >> Applied Statistical Mathematics submitted time 2023-12-04
Abstract： The report of the 20th National Congress of the Communist Party of China pointed out that we should deepen the comprehensive reform in the field of education and improve the school management and education evaluation system. The main body, object, content and effect of ideological work in colleges and universities in the new era present new characteristics, facing the challenge of high quality development of ideological and political work. In view of the political, contemporary, systematic and scientific grand system of ideological and political work, it is particularly important to make a scientific evaluation of the effect of ideological and political work. The implementation of digital transformation, the use of data science technology and statistical comprehensive evaluation theory to evaluate the effectiveness of ideological and political work in colleges and universities is an important means to improve the quality and efficiency of this work. It is an important practice to improve the academic level of ideological and political work and help to reshape ideological and political work by using big data technology.
Peer Review Status:Awaiting Review
Subjects: Statistics >> Applied Statistical Mathematics submitted time 2023-09-15
Abstract： 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.
Peer Review Status:Awaiting Review
Subjects: Statistics >> Economic Statistics Subjects: Statistics >> Applied Statistical Mathematics submitted time 2023-02-10
Abstract：
This paper investigates the solar term effect (anomaly) in China stock market as a supplementary to the existing literature of calender effect. Based on a regression framework, this paper verifies the existence of solar term effect in Shanghai Index in multiple dimensions: inter-solar-term analysis, full sample analysis at mean level and risk level as well as the turn of solar term effect. Several solar terms have been found to cause significant positive and negative value to the return such as solar term 1,3 and 4. and bring high volatility such as solar term 8, 11 and 14. The result is reliable and robust under the Extreme Bound Analysis and various assumptions of error’s distribution in IGARCH model. These findings give readers a new perspective to view calender effect under the influence of traditional Chinese culture that solar terms affect the market through affecting investors’ mood, expectation, enthusiasm, etc. which is a good evidence to the “Culture bonus hypothesis” proposed by Chen and Chien (2011) and the possible influence by the Chinese culture in other Asian markets (Yuan and Gupta, 2014).
Peer Review Status:Awaiting Review
Subjects: Statistics >> Applied Statistical Mathematics submitted time 2021-01-30
Abstract： " 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.
Peer Review Status:Awaiting Review
Subjects: Statistics >> Applied Statistical Mathematics submitted time 2019-04-22
Abstract： 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.
Peer Review Status:Awaiting Review
Hits
Hits
Downloads
Comment