Journal of Applied Mathematics Statistics and Informatics最新文献_第2页

A Short Note on Two Diophantine Equations 9 x – 3y = z2 and 13x – 7y = z2 关于两个丢番图方程9x - 3y = z2和13x - 7y = z2的注记

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2023-01-01 DOI: 10.22457/jmi.v24a02215

S. Tadee

引用次数: 0

Sum Augmented and Multiplicative Sum Augmented Indices of Some Nanostructures 一些纳米结构的和增和和乘增和指数

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2023-01-01 DOI: 10.22457/jmi.v24a03219

V. Kulli

引用次数: 1

Trichotomization with two cutoff values using Kruskal-Wallis test by minimum P-value approach 使用最小P值法进行Kruskal-Wallis检验的具有两个截断值的除毛

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-12-01 DOI: 10.2478/jamsi-2022-0010

T. Ogura, C. Shiraishi

{"title":"Trichotomization with two cutoff values using Kruskal-Wallis test by minimum P-value approach","authors":"T. Ogura, C. Shiraishi","doi":"10.2478/jamsi-2022-0010","DOIUrl":"https://doi.org/10.2478/jamsi-2022-0010","url":null,"abstract":"Abstract In clinical trials, age is often converted to binary data by the cutoff value. However, when looking at a scatter plot for a group of patients whose age is larger than or equal to the cutoff value, age and outcome may not be related. If the group whose age is greater than or equal to the cutoff value is further divided into two groups, the older of the two groups may appear to be at lower risk. In this case, it may be necessary to further divide the group of patients whose age is greater than or equal to the cutoff value into two groups. This study provides a method for determining which of the two or three groups is the best split. The following two methods are used to divide the data. The existing method, the Wilcoxon-Mann-Whitney test by minimum P-value approach, divides data into two groups by one cutoff value. A new method, the Kruskal-Wallis test by minimum P-value approach, divides data into three groups by two cutoff values. Of the two tests, the one with the smaller P-value is used. Because this was a new decision procedure, it was tested using Monte Carlo simulations (MCSs) before application to the available COVID-19 data. The MCS results showed that this method performs well. In the COVID-19 data, it was optimal to divide into three groups by two cutoff values of 60 and 70 years old. By looking at COVID-19 data separated into three groups according to the two cutoff values, it was confirmed that each group had different features. We provided the R code that can be used to replicate the results of this manuscript. Another practical example can be performed by replacing x and y with appropriate ones.","PeriodicalId":43016,"journal":{"name":"Journal of Applied Mathematics Statistics and Informatics","volume":"18 1","pages":"19 - 32"},"PeriodicalIF":0.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43936917","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

Coefficient inequalities for a subclass of analytic functions associated with exponential function 与指数函数相关的解析函数子类的系数不等式

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-12-01 DOI: 10.2478/jamsi-2022-0009

G. Singh, G. Singh

引用次数: 0

A new generalized transmuted distribution 一种新的广义变形分布

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-12-01 DOI: 10.2478/jamsi-2022-0013

S. A. Wani, S. A. Dar

引用次数: 0

The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic growth model PSO-BP组合模型和GA-BP组合模型在中国和V4经济增长模型中的应用

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-12-01 DOI: 10.2478/jamsi-2022-0011

X. Gui, Michal Feckan, J. Wang

{"title":"The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic growth model","authors":"X. Gui, Michal Feckan, J. Wang","doi":"10.2478/jamsi-2022-0011","DOIUrl":"https://doi.org/10.2478/jamsi-2022-0011","url":null,"abstract":"Abstract This paper adopts different optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO-Algorithm) to train Back-Propagation (BP) neural networks, fits the Chinese, the Czech, Slovak, Hungarian, and Polish gross domestic product (GDP) growth model (from 1995 to 2020) and makes short-term simulation predictions. We use the PSO-Algorithm and GA with strong global search ability to optimize the weights and thresholds of the network, combine them with the BP neural network, and apply the resulting Particle Swarm Optimization Back-Propagation (PSO-BP) combined model or Genetic-Algorithm Back-Propagation (GA-BP) combined model to allow the network to achieve fast convergence. Besides, we also compare the above two hybrid models with standard multivariate regression model and BP neural network with different initialization methods like normal uniform and Xavier for fitting and short-term simulation predictions. Finally, we obtain the excellent results that all the above models have achieved a good fitting effect and PSO-BP combined model on the whole has a smaller error than others in predicting GDP values. Through the technology of PSO-BP and GA-BP, we have a clearer understanding of the five countries gross domestic product growth trends, which is conducive to the government to make reasonable decisions on the economic development.","PeriodicalId":43016,"journal":{"name":"Journal of Applied Mathematics Statistics and Informatics","volume":"18 1","pages":"33 - 56"},"PeriodicalIF":0.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45090450","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

A heteroscedastic Bayesian model for method comparison data 方法比较数据的异方差贝叶斯模型

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-12-01 DOI: 10.2478/jamsi-2022-0012

S. Lakmali, Lakshika S. Nawarathna, P. Wijekoon

{"title":"A heteroscedastic Bayesian model for method comparison data","authors":"S. Lakmali, Lakshika S. Nawarathna, P. Wijekoon","doi":"10.2478/jamsi-2022-0012","DOIUrl":"https://doi.org/10.2478/jamsi-2022-0012","url":null,"abstract":"Abstract When implementing newly proposed methods on measurements taken from a human body in clinical trials, the researchers carefully consider whether the measurements have the maximum accuracy. Further, they verified the validity of the new method before being implemented in society. Method comparison evaluates the agreement between two continuous variables to determine whether those measurements agree on enough to interchange the methods. Special consideration of our work is a variation of the measurements with the magnitude of the measurement. We propose a method to evaluate the agreement of two methods when those are heteroscedastic using Bayesian inference since this method offers a more accurate, flexible, clear, and direct inference model using all available information. A simulation study was carried out to verify the characteristics and accuracy of the proposed model using different settings with different sample sizes. A gold particle dataset was analyzed to examine the practical viewpoint of the proposed model. This study shows that the coverage probabilities of all parameters are greater than 0.95. Moreover, all parameters have relatively low error values, and the simulation study implies the proposed model deals with the higher heteroscedasticity data with higher accuracy than others. In each setting, the model performs best when the sample size is 500.","PeriodicalId":43016,"journal":{"name":"Journal of Applied Mathematics Statistics and Informatics","volume":"18 1","pages":"57 - 75"},"PeriodicalIF":0.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43464371","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

A new ratio type estimator for computation of population mean under post-stratification 后分层下人口均值计算的一种新的比率型估计方法

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-05-01 DOI: 10.2478/jamsi-2022-0003

K. U. I. Rather, M. Jeelani, M. Shah, S. Rizvi, M. Sharma

引用次数: 1

Asymptotic expectation of protected node profile in random digital search trees 随机数字搜索树中受保护节点轮廓的渐近期望

IF 0.3

Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-05-01 DOI: 10.2478/jamsi-2022-0004

M. Javanian, R. I. Nabiyyi, J. Toofanpour, M. Q. Vahidi-Asl

引用次数: 0

Robust sparse principal component analysis: situation of full sparseness 鲁棒稀疏主成分分析:全稀疏情况

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Journal of Applied Mathematics Statistics and Informatics Pub Date : 2022-05-01 DOI: 10.2478/jamsi-2022-0001

B. Alkan, I. Ünaldi

{"title":"Robust sparse principal component analysis: situation of full sparseness","authors":"B. Alkan, I. Ünaldi","doi":"10.2478/jamsi-2022-0001","DOIUrl":"https://doi.org/10.2478/jamsi-2022-0001","url":null,"abstract":"Abstract Principal Component Analysis (PCA) is the main method of dimension reduction and data processing when the dataset is of high dimension. Therefore, PCA is a widely used method in almost all scientific fields. Because PCA is a linear combination of the original variables, the interpretation process of the analysis results is often encountered with some difficulties. The approaches proposed for solving these problems are called to as Sparse Principal Component Analysis (SPCA). Sparse approaches are not robust in existence of outliers in the data set. In this study, the performance of the approach proposed by Croux et al. (2013), which combines the advantageous properties of SPCA and Robust Principal Component Analysis (RPCA), will be examined through one real and three artificial datasets in the situation of full sparseness. In the light of the findings, it is recommended to use robust sparse PCA based on projection pursuit in analyzing the data. Another important finding obtained from the study is that the BIC and TPO criteria used in determining lambda are not much superior to each other. We suggest choosing one of these two criteria that give an optimal result.","PeriodicalId":43016,"journal":{"name":"Journal of Applied Mathematics Statistics and Informatics","volume":"18 1","pages":"5 - 20"},"PeriodicalIF":0.3,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41958636","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