Pub Date : 2019-02-11DOI: 10.15406/bbij.2019.08.00264
O. Maxwell, Agu Friday, Nwokike Chukwudike, Francis Runyi, Offorha Bright
Various distributions have been proposed to serve as models for wide applications on data from different real-life situations through the extension of existing distribution. This has been achieved in various ways. The Lomax distribution also called “Pareto type II” is a special case of the generalized beta distribution of the second kind,1 and can be seen in many application areas, such as actuarial science, economics, biological sciences, engineering, lifetime and reliability modeling and so on.2 This heavy duty distribution is considered useful as an alternative distribution to survival problems and life-testing in engineering and survival analysis.3 Inverse Lomax distribution is a member of the inverted family of distributions and discovered to be very flexible in analyzing situations with a realized non-monotonic failure rate.4 If a random variable X has a Lomax distribution, then 1 = Y X has an inverse Lomax Distribution. Thus, a random variable X is said to have an Inverted Lomax distribution if the corresponding probability density function and cumulative density function are given by Yadav et al.5
通过对现有分布的扩展，提出了各种分布作为模型，以广泛应用于来自不同实际情况的数据。这是通过各种方式实现的。Lomax分布也称为“Pareto II型”，是第二类广义beta分布的特例1，在精算科学、经济学、生物科学、工程、寿命和可靠性建模等许多应用领域中都可以看到这种重负荷分布被认为是工程和生存分析中生存问题和寿命测试的一种替代分布逆Lomax分布是逆分布族中的一员，在分析已实现非单调故障率的情况时被发现是非常灵活的如果随机变量X具有洛max分布，则1 = Y X具有逆洛max分布。因此，如果对应的概率密度函数和累积密度函数由Yadav等人给出，则称随机变量X具有倒Lomax分布
{"title":"A theoretical analysis of the odd generalized exponentiated inverse Lomax distribution","authors":"O. Maxwell, Agu Friday, Nwokike Chukwudike, Francis Runyi, Offorha Bright","doi":"10.15406/bbij.2019.08.00264","DOIUrl":"https://doi.org/10.15406/bbij.2019.08.00264","url":null,"abstract":"Various distributions have been proposed to serve as models for wide applications on data from different real-life situations through the extension of existing distribution. This has been achieved in various ways. The Lomax distribution also called “Pareto type II” is a special case of the generalized beta distribution of the second kind,1 and can be seen in many application areas, such as actuarial science, economics, biological sciences, engineering, lifetime and reliability modeling and so on.2 This heavy duty distribution is considered useful as an alternative distribution to survival problems and life-testing in engineering and survival analysis.3 Inverse Lomax distribution is a member of the inverted family of distributions and discovered to be very flexible in analyzing situations with a realized non-monotonic failure rate.4 If a random variable X has a Lomax distribution, then 1 = Y X has an inverse Lomax Distribution. Thus, a random variable X is said to have an Inverted Lomax distribution if the corresponding probability density function and cumulative density function are given by Yadav et al.5","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88193163","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 : 2019-01-23DOI: 10.15406/bbij.2019.08.00263
Othmar W. Winkler
The accumulated frequencies of the quantitative variables of socio-economic statistical data, the ogive, are treated , if at all, as a curiosity and only as an instruction of how to rearranging the frequencies of the distribution. The meaning of the result, an ogive, is not explained nor is its interpretation attempted. It is the purpose of this paper to make sense of cumulative frequency distributions, revealing features of the concrete local-historical situations of society, described by the data, that otherwise would remain unnoticed.
{"title":"Interpreting the cumulative frequency distribution of Socio-Economic data","authors":"Othmar W. Winkler","doi":"10.15406/bbij.2019.08.00263","DOIUrl":"https://doi.org/10.15406/bbij.2019.08.00263","url":null,"abstract":"The accumulated frequencies of the quantitative variables of socio-economic statistical data, the ogive, are treated , if at all, as a curiosity and only as an instruction of how to rearranging the frequencies of the distribution. The meaning of the result, an ogive, is not explained nor is its interpretation attempted. It is the purpose of this paper to make sense of cumulative frequency distributions, revealing features of the concrete local-historical situations of society, described by the data, that otherwise would remain unnoticed.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84628233","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 : 2019-01-21DOI: 10.15406/bbij.2019.08.00262
R. Shanker, Berhane Abebe, Mussie Tesfay, Tesfalem Eyob
The statistical properties, estimation of parameter and application of Lindley distribution are discussed in Ghitany et al.,4 Shanker et al.,5 have detailed study on applications of Lindley distribution and exponential distribution to model real lifetime datasets from engineering and biomedical sciences. Since Rama distribution has only one parameter, it has less flexibility to model data of varying natures. In the present paper an attempt has been made to derive twoparameter power Rama distribution which includes one parameter Rama distribution as particular cases as power transformation of Rama distribution. The shapes of the density, moments, hazard rate function, and mean residual life function of the distribution have been discussed. The maximum likelihood estimation has been explained. The goodness of fit of the proposed distribution has been discussed with two real lifetime dataset and fit shows quite satisfactory fit over other one parameter and two-parameter lifetime distributions.
{"title":"A two-parameter power Rama distribution with properties and applications","authors":"R. Shanker, Berhane Abebe, Mussie Tesfay, Tesfalem Eyob","doi":"10.15406/bbij.2019.08.00262","DOIUrl":"https://doi.org/10.15406/bbij.2019.08.00262","url":null,"abstract":"The statistical properties, estimation of parameter and application of Lindley distribution are discussed in Ghitany et al.,4 Shanker et al.,5 have detailed study on applications of Lindley distribution and exponential distribution to model real lifetime datasets from engineering and biomedical sciences. Since Rama distribution has only one parameter, it has less flexibility to model data of varying natures. In the present paper an attempt has been made to derive twoparameter power Rama distribution which includes one parameter Rama distribution as particular cases as power transformation of Rama distribution. The shapes of the density, moments, hazard rate function, and mean residual life function of the distribution have been discussed. The maximum likelihood estimation has been explained. The goodness of fit of the proposed distribution has been discussed with two real lifetime dataset and fit shows quite satisfactory fit over other one parameter and two-parameter lifetime distributions.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82070333","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 : 2019-01-09DOI: 10.15406/bbij.2019.08.00261
Getachew Tekle
{"title":"Application of GLM (logistic regression) on serological data of malaria infection","authors":"Getachew Tekle","doi":"10.15406/bbij.2019.08.00261","DOIUrl":"https://doi.org/10.15406/bbij.2019.08.00261","url":null,"abstract":"","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73241241","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 : 2018-12-19DOI: 10.15406/bbij.2018.07.00260
Tu Xu, Danting Zhu
In oncology randomized clinical trials, the time-to-event(TTE) type of endpoints such as progression-free survival (PFS) and overall survival(OS), are commonly used as the primary or key secondary endpoints for comparing the experimental treatment and active control/ placebo. In practice, the proportional hazard (PH) is usually assumed to characterize the treatment benefit over time of TTE endpoints and calculate the required sample size. With the PH assumption, the hazard ratio (HR) between treatment arms is a constant over time, and the corresponding testing hypothesis is expressed as
{"title":"A review of statistical methods on testing time-to-event data","authors":"Tu Xu, Danting Zhu","doi":"10.15406/bbij.2018.07.00260","DOIUrl":"https://doi.org/10.15406/bbij.2018.07.00260","url":null,"abstract":"In oncology randomized clinical trials, the time-to-event(TTE) type of endpoints such as progression-free survival (PFS) and overall survival(OS), are commonly used as the primary or key secondary endpoints for comparing the experimental treatment and active control/ placebo. In practice, the proportional hazard (PH) is usually assumed to characterize the treatment benefit over time of TTE endpoints and calculate the required sample size. With the PH assumption, the hazard ratio (HR) between treatment arms is a constant over time, and the corresponding testing hypothesis is expressed as","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81563063","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 : 2018-12-14DOI: 10.15406/bbij.2018.07.00259
J. Subramani
information on auxiliary variables are effectively used to improve the efficiency of the simple random sampling without replacement (SRSWOR) estimator of the population mean. As the results, ratio, product and regression estimators are widely utilized in many situations, see for example Cochran1 and Murthy.2 Modified ratio estimators are developed to achieve further improvements on the ratio estimator with known parameters of the auxiliary variable, which include Sisodia & Dwivedi3 with known Co-efficient of Variation, Singh et.,4 with known Kurtosis, Yan & Tian5 with the known Skewness, Subramani and Kumarapandiyan6-9 with the known median and its linear combinations with the other known parameters. This paper deals with the two parameter modified ratio estimators with known correlation coefficient and skewness of two auxiliary variables
{"title":"Two parameter modified ratio estimators with two auxiliary variables for the estimation of finite population mean","authors":"J. Subramani","doi":"10.15406/bbij.2018.07.00259","DOIUrl":"https://doi.org/10.15406/bbij.2018.07.00259","url":null,"abstract":"information on auxiliary variables are effectively used to improve the efficiency of the simple random sampling without replacement (SRSWOR) estimator of the population mean. As the results, ratio, product and regression estimators are widely utilized in many situations, see for example Cochran1 and Murthy.2 Modified ratio estimators are developed to achieve further improvements on the ratio estimator with known parameters of the auxiliary variable, which include Sisodia & Dwivedi3 with known Co-efficient of Variation, Singh et.,4 with known Kurtosis, Yan & Tian5 with the known Skewness, Subramani and Kumarapandiyan6-9 with the known median and its linear combinations with the other known parameters. This paper deals with the two parameter modified ratio estimators with known correlation coefficient and skewness of two auxiliary variables","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81411461","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 : 2018-11-30DOI: 10.15406/bbij.2018.07.00258
S. Tzortzios
It is reported by FAO that between now and 2050, the world’s population will increase by one-third. Most of these additional 2 billion people will live in developing countries. At the same time, more people will be living in cities. If current income and consumption growth trends continue, FAO estimates that agricultural production will have to increase by 60 percent by 2050 to satisfy the expected demands for food and feed. Agriculture must therefore transform itself if it is to feed a growing global population and provide the basis for economic growth and poverty reduction. Climate change will make this task more difficult under a business-as-usual scenario, due to adverse impacts on agriculture, requiring spiraling adaptation and related costs.
{"title":"Applying biometry to increase productivity in rural and under-developed areas and maximize the potential of local natural resources for global benefit","authors":"S. Tzortzios","doi":"10.15406/bbij.2018.07.00258","DOIUrl":"https://doi.org/10.15406/bbij.2018.07.00258","url":null,"abstract":"It is reported by FAO that between now and 2050, the world’s population will increase by one-third. Most of these additional 2 billion people will live in developing countries. At the same time, more people will be living in cities. If current income and consumption growth trends continue, FAO estimates that agricultural production will have to increase by 60 percent by 2050 to satisfy the expected demands for food and feed. Agriculture must therefore transform itself if it is to feed a growing global population and provide the basis for economic growth and poverty reduction. Climate change will make this task more difficult under a business-as-usual scenario, due to adverse impacts on agriculture, requiring spiraling adaptation and related costs.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82998110","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 : 2018-11-28DOI: 10.15406/bbij.2018.07.00257
R. Sadraei, V. Sadeghi, Maryam Sadraei
In this study, we investigate the development of biotechnology from academic entrepreneurship to industrial entrepreneurship. Several factors were studied in order to explore their influence on evolution of Biotechnology in three different context such as United States, Europe area (Italy) for this case study. In the present work some flexible factors were considered due to its transferability to other fields of broad observations regarding entrepreneuring.
{"title":"Biotechnology revolution from academic entrepreneurship to industrial: chemo-entrepreneurship","authors":"R. Sadraei, V. Sadeghi, Maryam Sadraei","doi":"10.15406/bbij.2018.07.00257","DOIUrl":"https://doi.org/10.15406/bbij.2018.07.00257","url":null,"abstract":"In this study, we investigate the development of biotechnology from academic entrepreneurship to industrial entrepreneurship. Several factors were studied in order to explore their influence on evolution of Biotechnology in three different context such as United States, Europe area (Italy) for this case study. In the present work some flexible factors were considered due to its transferability to other fields of broad observations regarding entrepreneuring.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74939499","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 : 2018-11-23DOI: 10.15406/bbij.2018.07.00256
Ieren Tg
{"title":"Modeling lifetime data with Weibull-Lindley distribution","authors":"Ieren Tg","doi":"10.15406/bbij.2018.07.00256","DOIUrl":"https://doi.org/10.15406/bbij.2018.07.00256","url":null,"abstract":"","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82676790","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 : 2018-11-20DOI: 10.15406/BBIJ.2018.07.00255
J. Varghese, K. K. Jose
Circular or directional data arise in different ways. The two main ways correspond to the two principal circular measuring instruments, the compass and the clock. Typical observations measured by the compass include wind directions and directions of migrating birds. Data of a similar type arise from measurements by spirit level or protractor. Typical observations measured by the clock include the arrival times (on a 24-hour clock) of patients at a casualty unit in a hospital. Data of a similar type arise as times of year or times of month of appropriate events. A circular observation can be regarded as a point on a circle of unit radius, or a unit vector (i.e. a direction) in the plane. Once an initial direction and an orientation of the circle have been chosen, each circular observation can be specified by the angle from the initial direction to the point on the circle corresponding to the observation.
{"title":"Wrapped hb-skewed laplace distribution and its application in meteorology","authors":"J. Varghese, K. K. Jose","doi":"10.15406/BBIJ.2018.07.00255","DOIUrl":"https://doi.org/10.15406/BBIJ.2018.07.00255","url":null,"abstract":"Circular or directional data arise in different ways. The two main ways correspond to the two principal circular measuring instruments, the compass and the clock. Typical observations measured by the compass include wind directions and directions of migrating birds. Data of a similar type arise from measurements by spirit level or protractor. Typical observations measured by the clock include the arrival times (on a 24-hour clock) of patients at a casualty unit in a hospital. Data of a similar type arise as times of year or times of month of appropriate events. A circular observation can be regarded as a point on a circle of unit radius, or a unit vector (i.e. a direction) in the plane. Once an initial direction and an orientation of the circle have been chosen, each circular observation can be specified by the angle from the initial direction to the point on the circle corresponding to the observation.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84787080","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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