International Journal of Statistical Distributions and Applications最新文献_第3页

Estimating Average Variation About the Population Mean Using Geometric Measure of Variation 用几何变异量估计总体均值的平均变异

International Journal of Statistical Distributions and Applications Pub Date : 2020-08-25 DOI: 10.11648/j.ijsd.20200602.11

Troon J. Benedict, Karanjah Anthony, Alilah Anekeya David

{"title":"Estimating Average Variation About the Population Mean Using Geometric Measure of Variation","authors":"Troon J. Benedict, Karanjah Anthony, Alilah Anekeya David","doi":"10.11648/j.ijsd.20200602.11","DOIUrl":"https://doi.org/10.11648/j.ijsd.20200602.11","url":null,"abstract":"Measures of dispersion are important statistical tool used to illustrate the distribution of datasets. These measures have allowed researchers to define the distribution of various datasets especially the measures of dispersion from the mean. Researchers and mathematicians have been able to develop measures of dispersion from the mean such as mean deviation, variance and standard deviation. However, these measures have been determined not to be perfect, for example, variance give average of squared deviation which differ in unit of measurement as the initial dataset, mean deviation gives bigger average deviation than the actual average deviation because it violates the algebraic laws governing absolute numbers, while standard deviation is affected by outliers and skewed datasets. As a result, there was a need to develop a more efficient measure of variation from the mean that would overcome these weaknesses. The aim of the paper was to estimate the average variation about the population mean using geometric measure of variation. The study was able to use the geometric measure of variation to estimate the average variation about the population mean for un-weighted datasets, weighted datasets, probability mass and probability density functions with finite intervals, however, the function faces serious integration problems when estimating the average deviation for probability density functions as a result of complexity in the integrations by parts involved and also integration on infinite intervals. Despite the challenge on probability density functions, the study was able to establish that the geometric measure of variation was able to overcome the challenges faced by the existing measures of variation about the population mean.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"184 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120972632","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

Private Premium of Endowment Last Survivor and Joint Life Insurance with Pareto Distribution 具有帕累托分布的养老最后遗属私人保费与共同人寿保险

International Journal of Statistical Distributions and Applications Pub Date : 2019-11-08 DOI: 10.11648/J.IJSD.20190504.11

Hasriati, Tumpal Parulian Nababan

{"title":"Private Premium of Endowment Last Survivor and Joint Life Insurance with Pareto Distribution","authors":"Hasriati, Tumpal Parulian Nababan","doi":"10.11648/J.IJSD.20190504.11","DOIUrl":"https://doi.org/10.11648/J.IJSD.20190504.11","url":null,"abstract":"This paper studies a dual life insurance premium is determined with the combined status of last survivor and joint life involving two insurance participants who have a kinship relationship such as husband and wife, brother and sister, which they work in the same agency. In determining the policy to be made by the life insurance does not require two policies to be made, but enough to have only one policy. So that by having one policy expected premiums paid by life insurance participants to life insurance companies will be smaller than if you have to pay in two policies. Determination of insurance premiums dual life to be paid by an insurance party participant based on the chance of death from both life insurance participants, stating a condition that will continue as long as there is at least one member who is still alive and will cease after the death of the last person of its member, and also is an ongoing condition se long time all members of a combination of several people can survive and will stop after one of its members first dies, to determine the single premium and annual premium using the cash value of the initial life annuity from dual life insurance. Whereas the initial annuity cash value is influenced by the interest rate and discount factor and is also influenced by the combined life opportunity of the two insurance participants. Furthermore, from the chance of life will be obtained the chance of dying In formulating the chance of dying the insurance participant is used the Pareto distribution and to obtain the parameter values in the Pareto distribution the maximum Likelihood method is used. In order to obtain the chance of death and can be used to calculate a single premium and annual premium.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"73 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131898469","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: 4

Extreme Value Modelling of Rainfall Using Poisson-generalized Pareto Distribution: A Case Study Tanzania 基于泊松-广义帕累托分布的降雨极值模型:以坦桑尼亚为例

International Journal of Statistical Distributions and Applications Pub Date : 2019-09-10 DOI: 10.11648/j.ijsd.20190503.14

E. Iyamuremye, J. Mung'atu, P. Mwita

{"title":"Extreme Value Modelling of Rainfall Using Poisson-generalized Pareto Distribution: A Case Study Tanzania","authors":"E. Iyamuremye, J. Mung'atu, P. Mwita","doi":"10.11648/j.ijsd.20190503.14","DOIUrl":"https://doi.org/10.11648/j.ijsd.20190503.14","url":null,"abstract":"Extreme rainfall events have caused significant damage to agriculture, ecology and infrastructure, disruption of human activities, injury and loss of life. They have also significant social, economical and environmental consequences because they considerably damage urban as well as rural areas. Early detection of extreme maximum rainfall helps to implement strategies and measures, before they occur. Extreme value theory has been used widely in modelling extreme rainfall and in various disciplines, such as financial markets, insurance industry, failure cases. Climatic extremes have been analysed by using either generalized extreme value (GEV) or generalized Pareto (GP) distributions which provides evidence of the importance of modelling extreme rainfall from different regions of the world. In this paper, we focus on Peak Over Thresholds approach where the Poisson-generalized Pareto distribution is considered as the proper distribution for the study of the exceedances. This research considers also use of the generalized Pareto (GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. The research used statistical techniques to fit models that used to predict extreme rainfall in Tanzania. The results indicate that the proposed Poisson-GP distribution provide a better fit to maximum monthly rainfall data. Further, the Poisson-GP models are able to estimate various return levels. Research found also a slowly increase in return levels for maximum monthly rainfall for higher return periods and further the intervals are increasingly wider as the return period is increasing.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131709492","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: 3

An Analysis of the Determinants of Fertility Differentials Amongst the Poorest Women Population in Kenya 肯尼亚最贫困妇女生育率差异的决定因素分析

International Journal of Statistical Distributions and Applications Pub Date : 2019-08-13 DOI: 10.11648/J.IJSD.20190503.13

R. M. Mutwiri

{"title":"An Analysis of the Determinants of Fertility Differentials Amongst the Poorest Women Population in Kenya","authors":"R. M. Mutwiri","doi":"10.11648/J.IJSD.20190503.13","DOIUrl":"https://doi.org/10.11648/J.IJSD.20190503.13","url":null,"abstract":"Fertility is one of the major elements in population dynamics that has the highest significant contribution towards population size and structure in the world. In Kenya, fertility levels have been on the decline from approximately 8.1 children in 1979 to 3.9 children in 2014 but still, it is considered high compared to the country’s target of 2.6 by 2030. This has potentially negative consequences to the economic growth and development of a country. The main objective of this study is to determine demographic, socio-economic and cultural factors that explain fertility differential among poor women of childbearing age. A binary logistic regression model was fitted to DHS 2014 data using SPSS Version16. The total number of women in childbearing age is based on 7,262 women who have at least one child and whose age ranges from 15 to 49 years. The majority of women were married 4685 (64.5%), followed by never and formally married 1522 (21.0%) and living with partner 1055 (14.5%) respectively). In the analyses, all the variables Region, women educational level, marital status, age at first marriage and age in 5-years group were found to have a significant effect on the total number of children ever born at a significance level of 5%. From the fitted logistic regression model, the estimated odds ratio for the variable region reference category is Nyanza/Western region. The value of the odds ratio exp(β) =1.060775, for the region that the odds of having TCEB greater than or equals to five children for the North Eastern region has 6.0775% more than women in Nyanza/Western Region (OR=1.060775, C.I=0.873716-1.287883) and its effect is statistically significant.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127723980","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: 5

Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model 肯尼亚内罗毕番茄批发价格预测:Sarima模型的时间序列分析

International Journal of Statistical Distributions and Applications Pub Date : 2019-08-06 DOI: 10.11648/J.IJSD.20190503.11

Robert Mathenge Mutwiri

{"title":"Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model","authors":"Robert Mathenge Mutwiri","doi":"10.11648/J.IJSD.20190503.11","DOIUrl":"https://doi.org/10.11648/J.IJSD.20190503.11","url":null,"abstract":"Price forecasting is more sensitive with vegetable crops due to their high nature of perishability and seasonality and is often used to make better-informed decisions and to manage price risk. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast price of tomatoes using monthly data for the period 1981 to 2013 obtained from the Ministry of Agriculture, Livestock and Fisheries (MALF) in the agribusiness department. Forecasting tomato prices was done using time series monthly average prices from January 2003 to December 2016. SARIMA (2, 1, 1) (1, 0, 1)12 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. The time series data of Tomatoes for wholesale markets in Nairobi are considered as the national average. The predictive ability tests RMSE = 32.063, MAPE = 125.251 and MAE = 22.3 showed that the model was appropriate for forecasting the price of tomatoes in Nairobi County, Kenya.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125732250","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: 7

On Transmuted Four Parameters Generalized Log-Logistic Distribution 变形四参数广义对数- logistic分布

International Journal of Statistical Distributions and Applications Pub Date : 2019-07-16 DOI: 10.11648/J.IJSD.20190502.12

Femi Samuel Adeyinka, A. Olapade

{"title":"On Transmuted Four Parameters Generalized Log-Logistic Distribution","authors":"Femi Samuel Adeyinka, A. Olapade","doi":"10.11648/J.IJSD.20190502.12","DOIUrl":"https://doi.org/10.11648/J.IJSD.20190502.12","url":null,"abstract":"In this article we transmute the four parameters generalized log-logistic distribution using quadratic rank transmutation map to develop a transmuted four parameters generalized log-logistic distribution. The quadratic rank transmutation map helps to introduce extra parameter into the baseline distribution to enhance more flexibility in the analysis of data in various disciplines such as reliability analysis in engineering, survival analysis, medicine, biological sciences, actuarial science, finance and insurance. The mathematical properties such as moments, quantile, mean, median, variance, skewness and kurtosis of this distribution are discussed. The reliability and hazard functions of the four parameters generalized log-logistic distribution are obtained. The probability density functions of the minimum and maximum order statistics of the four parameters generalized log-logistic distribution are established and the relationships between the probability density functions of the minimum and maximum order statistics of the parent model and the probability density functions of the four parameters generalized log-logistic distribution are considered. The parameter estimation is done by the maximum likelihood method. The flexibility of the model in statistical data analysis and its applicability is demonstrated by using it to fit relevant data. The study is concluded by demonstrating that the four parameters generalized log-logistic distribution has a better goodness of fit than its parent model. We hope this model will serve as an alternative to the existing ones in fitting positive real data.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091184","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: 9

Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R 甘蔗育种资料多变量基因型和环境互作双图分析

International Journal of Statistical Distributions and Applications Pub Date : 2019-06-26 DOI: 10.11648/J.IJSD.20190502.11

Ouma Victor Otieno, Onyango Nelson Owuor

{"title":"Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R","authors":"Ouma Victor Otieno, Onyango Nelson Owuor","doi":"10.11648/J.IJSD.20190502.11","DOIUrl":"https://doi.org/10.11648/J.IJSD.20190502.11","url":null,"abstract":"Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for GGE exists with usage depending on cost and knowhow. R open source analytical software ensures availability and the knowledge on the necessary packages is required thus the objective of the paper on utilization of GGE using R software in the evaluation of genotypes with presence GxEI. The application used secondary data of Kenyan Mtwapa series of 96 and 97 preliminary varietal trial stage 4 established under randomized complete block design (RCBD), consisting of 15 test genotypes and three controls in the environments of SONYsugar, Mumias and KibosF9 with the plant crop and ratoon crop cycles as seasons. The 2-way GEI data was handled using singular value decomposition (SVD) through the R package; GGEbiplot programmed scripts and graphical user interface (GUI) were used in ranking genotypes and environments, determining genotypes performance overall and in each environment, determining stabilities and adaptability of the genotypes and identifying mega trial environments. GGEbiplot unpacked the GEI through the principle components (PC) 1 and 2 that sufficiently explained 85.37% of the variations.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116028126","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: 2

Spatial Cumulative Probit Model: An Application to Poverty Classification and Mapping 空间累积概率模型在贫困分类与制图中的应用

International Journal of Statistical Distributions and Applications Pub Date : 2019-06-11 DOI: 10.11648/J.IJSD.20190501.14

R. Puurbalanta

{"title":"Spatial Cumulative Probit Model: An Application to Poverty Classification and Mapping","authors":"R. Puurbalanta","doi":"10.11648/J.IJSD.20190501.14","DOIUrl":"https://doi.org/10.11648/J.IJSD.20190501.14","url":null,"abstract":"Previous studies on household poverty classification have commonly dichotomized the dependent variable into non-poor or poor, and used binary models. This way, the most extreme categories of poverty, which are usually the main targets of interventions, are not identified. Moreover, expenditure data used to describe poverty is typically collected at several locations over large geographical domains. Local disturbances introduce spatial correlation, implying that global parameters (obtained via independence assumptions of standard statistical methods) cannot adequately describe site-specific conditions of the data. The objective, therefore, is to describe an appropriate method for ordered categorical data collected at geo-referenced locations over large geographical space. To achieve this, a model named Spatial Cumulative Probit Model (SCPM) was proposed. This model classified household poverty in an ordinal spatial framework. Bayesian inference was performed on data sampled by Markov Chain Monte Carlo (MCMC) algorithms. A test of model adequacy show that the SCPM is unbiased and attains a lower misclassification rate of 14.43% than the simple Cumulative Probit (CP) model with misclassification rate of 16.5% that ignores spatial dependence in the data. Overall, ‘savannah ecological zone’, ‘polygamous marriage’ and ‘rural location’ were the most powerful predictors of extreme poverty in Ghana. The prediction map, created by this study, identified positive correlation with respect to ‘poor’ and ‘extremely poor’ categories. Results of the model in this study can be considered a category and site-specific report that identifies all levels and sites of poverty for easy targeting, thus, avoiding the blanket approach that prefers the one-fits-it-all solution to the problem of poverty. Analysis was based on the Ghana Living Standards Survey (GLSS 6) dataset.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125569875","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

Estimation of Response Propensities Using the Previous Survey 利用先前的调查估计反应倾向

International Journal of Statistical Distributions and Applications Pub Date : 2019-06-04 DOI: 10.11648/J.IJSD.20190501.12

Miika Honkala

{"title":"Estimation of Response Propensities Using the Previous Survey","authors":"Miika Honkala","doi":"10.11648/J.IJSD.20190501.12","DOIUrl":"https://doi.org/10.11648/J.IJSD.20190501.12","url":null,"abstract":"Many surveys are carried out yearly, and the implementation of the surveys remains the same from year to year. Experience from a previous survey is useful when planning a new survey, because the response behavior usually remains quite the same in subsequent years. This paper studies how response propensities, estimated using the dataset of the previous survey, predict actual response rates. In this study, two consecutive datasets of the European Social Survey were available. The both datasets contained same register variables. Response propensities were estimated to the older dataset using a logistic regression model. Then the propensities were imputed to the newer dataset using a donor-recipient method. The imputation was based on the explanatory variables of the logistic regression model so that the donor and the recipient had the same values in the variables. Then it was examined if there was a connection between the imputed response propensities and actual response rates. The result was that the imputed response propensities predicted the response behavior quite well. People with low response propensities were often nonrespondents, and people with high response propensities were often respondents. Using the previous survey, it is possible to calculate response propensities for a new sample before the data collection of the survey has been started. Then challenging respondents are known before the data collection, and this information is useful for data collection.","PeriodicalId":427819,"journal":{"name":"International Journal of Statistical Distributions and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122625823","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

On Some Models for Wind Power Assessment in Yola, Nigeria 尼日利亚约拉风电评估模型研究

International Journal of Statistical Distributions and Applications Pub Date : 1900-01-01 DOI: 10.11648/j.ijsd.20210704.14

Gongsin Isaac Esbond, Funmilayo Westnand Oshogboye Saporu

引用次数: 1