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Machine learning and the James–Stein estimator 机器学习与James–Stein估计
IF 1.3 Q3 Mathematics Pub Date : 2023-06-30 DOI: 10.1007/s42081-023-00209-y
B. Efron
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引用次数: 1
Bayesian modeling via discrete nonparametric priors 基于离散非参数先验的贝叶斯建模
IF 1.3 Q3 Mathematics Pub Date : 2023-06-22 DOI: 10.1007/s42081-023-00210-5
Marta Catalano, A. Lijoi, Igor Prünster, T. Rigon
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引用次数: 0
Modeling Social Media Use and Anxiety Levels With Students’ Sleep Quality: Ordinal Logistic Regression 社交媒体使用和焦虑水平与学生睡眠质量的关系:有序逻辑回归
Q3 Mathematics Pub Date : 2023-06-07 DOI: 10.33369/jsds.v2i1.27259
Annisa Agustina .
The study tries to model sleep quality using ordinal logistic regression since the response variable is in the form of categorical data. The purpose of this study was to identify factors related to students' sleep quality based on social media usage variables and anxiety levels. One hundred and fifty students of SMAN 1 Tualang, Riau are selected with snowball technique and participated online. The result showed that there is a correlation between social media usage and anxiety over sleep quality. Social Media Usage Dependence degree on Sleep Quality was 59.3% and Anxiety level dependence degree on Sleep Quality was 65.3%. Ordinal logistical regression analysis showed that students who were inactive in social media had a good sleep quality, a rate of 0.462 times compared to students who were active in social media. Meanwhile, students with mild anxiety levels had a good sleep quality of 0.369 times compared to moderate anxiety levels.
由于响应变量采用分类数据的形式,本研究尝试使用有序逻辑回归对睡眠质量进行建模。本研究的目的是基于社交媒体使用变量和焦虑水平来确定与学生睡眠质量相关的因素。利用滚雪球技术,选出150名廖内省土朗地区sman1学生进行网上参与。结果表明,社交媒体的使用与睡眠质量焦虑之间存在相关性。社交媒体使用对睡眠质量的依赖程度为59.3%,焦虑水平对睡眠质量的依赖程度为65.3%。有序逻辑回归分析显示,不使用社交媒体的学生睡眠质量较好,是使用社交媒体的学生睡眠质量的0.462倍。同时,轻度焦虑学生的睡眠质量是中度焦虑学生的0.369倍。
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引用次数: 0
Goodness Test of Adaptability to Model of Technical Changes and Test of Forecasting Accuracy 技术变化模型适应性优度检验及预测精度检验
Q3 Mathematics Pub Date : 2023-06-06 DOI: 10.33369/jsds.v2i1.27257
Susiawati Susiawati, Budi Kurniawan
The technical coefficient input-output as an element of the technical coefficient matrix (A) is estimated to have good forecasts for the next several periods . By substituting the final demand (F) for the period into the Input Output (IO) model in the equation the total output for the period will be obtained from the forecasting results. The total output of forecasting results is then compared with the actual total output to see the magnitude of the deviation. In the regression equation, the coefficient of determination is a measure of “goodness of fit” which states how well the regression line explains the independent variable with the dependent variable. The test is carried out by regressing the technical coefficient of input-output in the year against the technical coefficient in the nth year in a simple linear regression equation . This test was conducted to see the validity of the technical coefficients in forecasting the IO model. This research is an empirical study that uses data from the Jambi Province Input Output Tables in 1998, 2007 and 2016, each of which has been collected in a common set to see the comparability between observation periods. The results show that the technical change model is quite well used for forecasting according to the assumption that the technical coefficient level is constant during the planning period. Meanwhile, the estimated output deviation tends to be higher than that of the actual data.
技术系数投入产出作为技术系数矩阵(A)的一个要素,估计对今后几个时期有很好的预测。通过将该时期的最终需求(F)代入方程中的输入输出(IO)模型,可以从预测结果中获得该时期的总产出。然后将预测结果的总产出与实际总产出进行比较,以查看偏差的大小。在回归方程中,决定系数是“拟合优度”的度量,它说明回归线如何很好地解释自变量与因变量。检验方法是将当年投入产出的技术系数与第n年的技术系数用简单的线性回归方程进行回归。这个测试是为了看到技术系数在预测IO模型中的有效性。本研究是一项实证研究,使用了1998年、2007年和2016年占碑省投入产出表的数据,每个数据都收集在一个共同的集合中,以观察观察期之间的可比性。结果表明,在规划期内技术系数水平不变的假设下,技术变化模型可以很好地用于预测。同时,估计的输出偏差往往高于实际数据。
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引用次数: 0
Panel Data Regression Analysis for Economic Growth Rate In Bengkulu Province 明古鲁省经济增长率的面板数据回归分析
Q3 Mathematics Pub Date : 2023-06-06 DOI: 10.33369/jsds.v2i1.27258
Filo Supianti
Panel data is a combination of time series data and cross section data. The analytical method used for panel data is panel data regression. One of the advantages of analysis using panel data regress One of the indicators to measure the development of the production of goods and services in an economic area in a given year against the value of the previous year which is calculated based on GDP/GRDP at constant prices is Economic Growth. The dependent variable in this study is the growth rate of GRDP. The independent variable in this study is IPM, TPAK, TPT. This study uses panel data regression analysis with the Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). The data processing in this study uses the R Studio application.
面板数据是时间序列数据和横截面数据的组合。面板数据的分析方法是面板数据回归。使用面板数据回归分析的优点之一衡量某一经济领域某一年的商品和服务生产与前一年的价值的发展的指标之一是经济增长,该指标是根据按不变价格计算的国内生产总值/国内生产总值计算的。本研究的因变量为GRDP的增长率。本研究的自变量为IPM、TPAK、TPT。本研究采用共同效应模型(Common Effect Model, CEM)、固定效应模型(Fixed Effect Model, FEM)和随机效应模型(Random Effect Model, REM)的面板数据回归分析。本研究中的数据处理使用了R Studio应用程序。
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引用次数: 0
Robust semiparametric modeling of mean and covariance in longitudinal data 纵向数据中均值和协方差的鲁棒半参数建模
IF 1.3 Q3 Mathematics Pub Date : 2023-06-02 DOI: 10.1007/s42081-023-00204-3
Mengfei Ran, Yihe Yang, Y. Kano
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引用次数: 0
Estimation and classification using progressive type-II censored samples from two exponential populations with a common location 使用具有共同位置的两个指数种群的渐进II型截尾样本进行估计和分类
IF 1.3 Q3 Mathematics Pub Date : 2023-05-17 DOI: 10.1007/s42081-023-00201-6
Pushkal Kumar, M. Tripathy
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引用次数: 0
Special feature: statistics for stochastic processes 特殊功能:随机过程的统计
IF 1.3 Q3 Mathematics Pub Date : 2023-05-17 DOI: 10.1007/s42081-023-00208-z
Masayuki Uchida
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引用次数: 0
Matrix quadratic risk of orthogonally invariant estimators for a normal mean matrix 正规均值矩阵正交不变估计的矩阵二次风险
IF 1.3 Q3 Mathematics Pub Date : 2023-05-15 DOI: 10.1007/s42081-023-00216-z
T. Matsuda
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引用次数: 0
Weighted scaling approach for metabolomics data analysis 代谢组学数据分析的加权标度方法
Q3 Mathematics Pub Date : 2023-05-10 DOI: 10.1007/s42081-023-00205-2
Biplab Biswas, Nishith Kumar, Md. Aminul Hoque, Md. Ashad Alam
Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influences the further statistical analysis. It is challenged to choose the appropriate scaling technique for downstream analysis to get accurate results or to make proper decision. Moreover, the existing scaling techniques are sensitive to outliers or extreme values. To fill the gap, our objective is to introduce a robust scaling approach that is not influenced by outliers as well as provides more accurate results for downstream analysis. Here, we introduced a new weighted scaling approach that is robust against outliers; however, no additional outlier detection/treatment step is needed in data preprocessing and also compared it with the conventional scaling and normalization techniques through artificial and real metabolomics datasets. We evaluated the performance of the proposed method in comparison to the other existing conventional scaling techniques using metabolomics data analysis in both the absence and presence of different percentages of outliers. Results show that in most cases, the proposed scaling technique is a better performer than the traditional scaling methods in both the absence and presence of outliers. The proposed method improves the further downstream metabolomics analysis. The R function of the proposed robust scaling method is available at https://github.com/nishithkumarpaul/robustScaling/blob/main/wscaling.R
系统变异是代谢组学数据分析中的一个常见问题。因此,不同的缩放和归一化技术被用于代谢组学数据分析的数据预处理。虽然文献中有几种标度方法,但标度、变换和/或归一化技术的选择影响了进一步的统计分析。如何选择合适的标度技术进行下游分析以获得准确的结果或做出正确的决策是一个挑战。此外,现有的标度技术对异常值或极值比较敏感。为了填补这一空白,我们的目标是引入一种不受异常值影响的鲁棒缩放方法,并为下游分析提供更准确的结果。在这里,我们引入了一种新的加权缩放方法,该方法对异常值具有鲁棒性;然而,在数据预处理中不需要额外的异常值检测/处理步骤,并通过人工和真实代谢组学数据集将其与传统的缩放和归一化技术进行了比较。我们评估了所提出的方法的性能,与其他现有的传统缩放技术相比,使用代谢组学数据分析,在没有和存在不同百分比的异常值的情况下。结果表明,在大多数情况下,无论异常值是否存在,本文提出的缩放技术都比传统的缩放方法具有更好的性能。该方法改进了进一步的下游代谢组学分析。所提出的鲁棒缩放方法的R函数可在https://github.com/nishithkumarpaul/robustScaling/blob/main/wscaling.R上获得
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引用次数: 0
期刊
Japanese Journal of Statistics and Data Science
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