Journal of the Korean Statistical Society最新文献_第5页

Return prediction by machine learning for the Korean stock market 通过机器学习预测韩国股票市场的回报率

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-12-20 DOI: 10.1007/s42952-023-00245-0

Wonwoo Choi, Seongho Jang, Sanghee Kim, Chayoung Park, Sunyoung Park, Seongjoo Song

{"title":"Return prediction by machine learning for the Korean stock market","authors":"Wonwoo Choi, Seongho Jang, Sanghee Kim, Chayoung Park, Sunyoung Park, Seongjoo Song","doi":"10.1007/s42952-023-00245-0","DOIUrl":"https://doi.org/10.1007/s42952-023-00245-0","url":null,"abstract":"In this study, we aim to forecast monthly stock returns and analyze factors influencing stock prices in the Korean stock market. To find a model that maximizes the cumulative return of the portfolio of stocks with high predicted returns, we use machine learning models such as linear models, tree-based models, neural networks, and learning to rank algorithms. We employ a novel validation metric which we call the Cumulative net Return of a Portfolio with top 10% predicted return (CRP10) for tuning hyperparameters to increase the cumulative return of the selected portfolio. CRP10 tends to provide higher cumulative returns compared to out-of-sample R-squared as a validation metric with the data that we used. Our findings indicate that Light Gradient Boosting Machine (LightGBM) and Gradient Boosted Regression Trees (GBRT) demonstrate better performance than other models when we apply a single model for the entire test period. We also take the strategy of changing the model on a yearly basis by assessing the best model annually and observed that it did not outperform the approach of using a single model such as LightGBM or GBRT for the entire period.","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138816752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatially integrated estimator of finite population total by integrating data from two independent surveys using spatial information 利用空间信息整合来自两个独立调查的数据，对有限人口总数进行空间整合估算

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-12-19 DOI: 10.1007/s42952-023-00244-1

Nobin Chandra Paul, Anil Rai, Tauqueer Ahmad, Ankur Biswas, Prachi Misra Sahoo

{"title":"Spatially integrated estimator of finite population total by integrating data from two independent surveys using spatial information","authors":"Nobin Chandra Paul, Anil Rai, Tauqueer Ahmad, Ankur Biswas, Prachi Misra Sahoo","doi":"10.1007/s42952-023-00244-1","DOIUrl":"https://doi.org/10.1007/s42952-023-00244-1","url":null,"abstract":"A major goal of survey sampling is finite population inference. In recent years, large-scale survey programs have encountered many practical challenges which include higher data collection cost, increasing non-response rate, increasing demand for disaggregated level statistics and desire for timely estimates. Data integration is a new field of research that provides a timely solution to these above-mentioned challenges by integrating data from multiple surveys. Now, it is possible to develop a framework that can efficiently combine information from several surveys to obtain more precise estimates of population parameters. In many surveys, parameters of interest are often spatial in nature, which means, the relationship between the study variable and covariates varies across all locations in the study area and this situation is referred as spatial non-stationarity. Hence, there is a need of a sampling methodology that can efficiently tackle this spatial non-stationarity problem and can be able to integrate this spatially referenced data to get more detailed information. In this study, a Geographically Weighted Spatially Integrated (GWSI) estimator of finite population total was developed by integrating data from two independent surveys using spatial information. The statistical properties of the proposed spatially integrated estimator were then evaluated empirically through a spatial simulation study. Three different spatial populations were generated having high spatial autocorrelation. The proposed spatially integrated estimator performed better than usual design-based estimator under all three populations. Furthermore, a Spatial Proportionate Bootstrap (SPB) method was developed for variance estimation of the proposed spatially integrated estimator.","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138744869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical integration of allele frequencies from several organizations 统计整合多个组织的等位基因频率

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-12-18 DOI: 10.1007/s42952-023-00243-2

Su Jin Jeong, Hyo-jung Lee, Soong Deok Lee, Su Jeong Park, Seung Hwan Lee, Jae Won Lee

{"title":"Statistical integration of allele frequencies from several organizations","authors":"Su Jin Jeong, Hyo-jung Lee, Soong Deok Lee, Su Jeong Park, Seung Hwan Lee, Jae Won Lee","doi":"10.1007/s42952-023-00243-2","DOIUrl":"https://doi.org/10.1007/s42952-023-00243-2","url":null,"abstract":"Genetic evidence, especially evidence based on short tandem repeats, is of paramount importance for human identification in forensic inferences. In recent years, the identification of kinship using DNA evidence has drawn much attention in various fields. In particular, it is employed, using a criminal database, to confirm blood relations in forensics. The interpretation of the likelihood ratio when identifying an individual or a relationship depends on the allele frequencies that are used, and thus, it is crucial to obtain an accurate estimate of allele frequency. Each organization such as Supreme Prosecutors’ Office and Korean National Police Agency in Korea provides different statistical interpretations due to differing estimations of the allele frequency, which can lead to confusion in forensic identification. Therefore, it is very important to estimate allele frequency accurately, and doing so requires a certain amount of information. However, simply using a weighted average for each allele frequency may not be sufficient to determine biological independence. In this study, we propose a new statistical method for estimating allele frequency by integrating the data obtained from several organizations, and we analyze biological independence and differences in allele frequency relative to the weighted average of allele frequencies in various subgroups. Finally, our proposed method is illustrated using real data from 576 Korean individuals.","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138717017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of repeated measurements using bias corrected Euclidean distance discriminant function 利用偏差校正欧氏距离判别函数对重复测量进行分类

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-12-12 DOI: 10.1007/s42952-023-00246-z

Edward Kanuti Ngailo, Saralees Nadarajah

{"title":"Classification of repeated measurements using bias corrected Euclidean distance discriminant function","authors":"Edward Kanuti Ngailo, Saralees Nadarajah","doi":"10.1007/s42952-023-00246-z","DOIUrl":"https://doi.org/10.1007/s42952-023-00246-z","url":null,"abstract":"This paper introduces a novel approach for approximating misclassification probabilities in Euclidean distance classifier when the group means exhibit a bilinear structure such as in the growth curve model first proposed by Potthoff and Roy (Biometrika 51:313–326, 1964). Initially, by leveraging certain statistical relationships, we establish two general results for the improved Euclidean discriminant function in both weighted and unweighted growth curve mean structures. We derive these approximations for the expected misclassification probabilities with respect to the distribution of the improved Euclidean discriminant function. Additionally, we compare the misclassification probabilities of the improved Euclidean discriminant function, the standard Euclidean discriminant function, and the linear discriminant function. It is important to note that in cases where the mean structure is weighted, a higher number of repeated measurements yields better classification results with the improved Euclidean discriminant function and the standard Euclidean discriminant function, allowing for more information to be acquired, as opposed to the linear discriminant function, which performs well with a smaller number of repeated measurements. Furthermore, we evaluate the accuracy of the suggested approximations by Monte Carlo simulations.","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse functional linear models via calibrated concave-convex procedure 稀疏函数线性模型通过校准凹-凸程序

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-12-03 DOI: 10.1007/s42952-023-00242-3

Young Joo Lee, Yongho Jeon

引用次数: 0

Nonparametric longitudinal regression model to analyze shape data using the Procrustes rotation 利用非参数纵向回归模型分析形状数据的Procrustes旋转

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-12-03 DOI: 10.1007/s42952-023-00241-4

Meisam Moghimbeygi, Mousa Golalizadeh

引用次数: 0

Variable selection for semiparametric accelerated failure time models with nonignorable missing data 具有不可忽略缺失数据的半参数加速失效时间模型的变量选择

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-11-19 DOI: 10.1007/s42952-023-00238-z

Tianqing Liu, Xiaohui Yuan, Liuquan Sun

引用次数: 0

Robust and Efficient derivative estimation under correlated errors 相关误差下稳健高效的导数估计

IF 0.6 4区数学

Journal of the Korean Statistical Society Pub Date : 2023-11-18 DOI: 10.1007/s42952-023-00240-5

Deru Kong, Wei Shen, Shengli Zhao, WenWu Wang

引用次数: 0

Asymptotic bias of the $$ell _2$$-regularized error variance estimator $$ell _2$$ -正则化误差方差估计量的渐近偏差

4区数学

Journal of the Korean Statistical Society Pub Date : 2023-11-14 DOI: 10.1007/s42952-023-00239-y

Semin Choi, Gunwoong Park

引用次数: 0

A review on concomitants of order statistics and its application in parameter estimation under ranked set sampling 序统计量的伴随量及其在排序集抽样参数估计中的应用综述

4区数学

Journal of the Korean Statistical Society Pub Date : 2023-11-13 DOI: 10.1007/s42952-023-00235-2

Rohan D. Koshti, Kirtee K. Kamalja

引用次数: 0