Japanese Journal of Statistics and Data Science最新文献_第3页

Machine learning and the James–Stein estimator 机器学习与James–Stein估计

IF 1.3

Japanese Journal of Statistics and Data Science Pub Date : 2023-06-30 DOI: 10.1007/s42081-023-00209-y

B. Efron

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-06-22 DOI: 10.1007/s42081-023-00210-5

Marta Catalano, A. Lijoi, Igor Prünster, T. Rigon

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-06-07 DOI: 10.33369/jsds.v2i1.27259

Annisa Agustina .

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-06-06 DOI: 10.33369/jsds.v2i1.27257

Susiawati Susiawati, Budi Kurniawan

{"title":"Goodness Test of Adaptability to Model of Technical Changes and Test of Forecasting Accuracy","authors":"Susiawati Susiawati, Budi Kurniawan","doi":"10.33369/jsds.v2i1.27257","DOIUrl":"https://doi.org/10.33369/jsds.v2i1.27257","url":null,"abstract":"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.","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135602375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-06-06 DOI: 10.33369/jsds.v2i1.27258

Filo Supianti

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-06-02 DOI: 10.1007/s42081-023-00204-3

Mengfei Ran, Yihe Yang, Y. Kano

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-05-17 DOI: 10.1007/s42081-023-00201-6

Pushkal Kumar, M. Tripathy

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-05-17 DOI: 10.1007/s42081-023-00208-z

Masayuki Uchida

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-05-15 DOI: 10.1007/s42081-023-00216-z

T. Matsuda

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

Japanese Journal of Statistics and Data Science Pub Date : 2023-05-10 DOI: 10.1007/s42081-023-00205-2

Biplab Biswas, Nishith Kumar, Md. Aminul Hoque, Md. Ashad Alam

{"title":"Weighted scaling approach for metabolomics data analysis","authors":"Biplab Biswas, Nishith Kumar, Md. Aminul Hoque, Md. Ashad Alam","doi":"10.1007/s42081-023-00205-2","DOIUrl":"https://doi.org/10.1007/s42081-023-00205-2","url":null,"abstract":"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","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135622541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0