Pub Date : 2023-06-30DOI: 10.1007/s42081-023-00209-y
B. Efron
{"title":"Machine learning and the James–Stein estimator","authors":"B. Efron","doi":"10.1007/s42081-023-00209-y","DOIUrl":"https://doi.org/10.1007/s42081-023-00209-y","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44673723","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}
Pub Date : 2023-06-22DOI: 10.1007/s42081-023-00210-5
Marta Catalano, A. Lijoi, Igor Prünster, T. Rigon
{"title":"Bayesian modeling via discrete nonparametric priors","authors":"Marta Catalano, A. Lijoi, Igor Prünster, T. Rigon","doi":"10.1007/s42081-023-00210-5","DOIUrl":"https://doi.org/10.1007/s42081-023-00210-5","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44903176","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}
Pub Date : 2023-06-07DOI: 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.
{"title":"Modeling Social Media Use and Anxiety Levels With Students’ Sleep Quality: Ordinal Logistic Regression","authors":"Annisa Agustina .","doi":"10.33369/jsds.v2i1.27259","DOIUrl":"https://doi.org/10.33369/jsds.v2i1.27259","url":null,"abstract":"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.","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135494338","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}
Pub Date : 2023-06-06DOI: 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.
{"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}
Pub Date : 2023-06-06DOI: 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.
{"title":"Panel Data Regression Analysis for Economic Growth Rate In Bengkulu Province","authors":"Filo Supianti","doi":"10.33369/jsds.v2i1.27258","DOIUrl":"https://doi.org/10.33369/jsds.v2i1.27258","url":null,"abstract":"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.","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"26 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":"135602376","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}
Pub Date : 2023-06-02DOI: 10.1007/s42081-023-00204-3
Mengfei Ran, Yihe Yang, Y. Kano
{"title":"Robust semiparametric modeling of mean and covariance in longitudinal data","authors":"Mengfei Ran, Yihe Yang, Y. Kano","doi":"10.1007/s42081-023-00204-3","DOIUrl":"https://doi.org/10.1007/s42081-023-00204-3","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49103888","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}
Pub Date : 2023-05-17DOI: 10.1007/s42081-023-00201-6
Pushkal Kumar, M. Tripathy
{"title":"Estimation and classification using progressive type-II censored samples from two exponential populations with a common location","authors":"Pushkal Kumar, M. Tripathy","doi":"10.1007/s42081-023-00201-6","DOIUrl":"https://doi.org/10.1007/s42081-023-00201-6","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"6 1","pages":"243 - 278"},"PeriodicalIF":1.3,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46128546","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}
Pub Date : 2023-05-17DOI: 10.1007/s42081-023-00208-z
Masayuki Uchida
{"title":"Special feature: statistics for stochastic processes","authors":"Masayuki Uchida","doi":"10.1007/s42081-023-00208-z","DOIUrl":"https://doi.org/10.1007/s42081-023-00208-z","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"6 1","pages":"301 - 303"},"PeriodicalIF":1.3,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46320566","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}
Pub Date : 2023-05-15DOI: 10.1007/s42081-023-00216-z
T. Matsuda
{"title":"Matrix quadratic risk of orthogonally invariant estimators for a normal mean matrix","authors":"T. Matsuda","doi":"10.1007/s42081-023-00216-z","DOIUrl":"https://doi.org/10.1007/s42081-023-00216-z","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41369535","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}
Pub Date : 2023-05-10DOI: 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
{"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}
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