Pub Date : 2012-12-01DOI: 10.5923/J.STATISTICS.20120205.01
K. K. Singh, Brijesh P Singh, Kushagra Gupta, S. Dst-Cim
Fert ility plays an important ro le in any demographic transition and total fert ility rate (TFR) is one of the basic measurements of fert ility. Due to non-availability of co mp lete and reliable data, a large nu mber of indirect techniques have been developed to estimate the demographic parameters with incomp lete data. So me of these techniques are based on utilizing the data fro m stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. In the present paper an indirect method has been proposed to estimate the TFR using regression analysis. In these types of analysis the most serious problem is the choice of predictor variable. If the choice of predictor variab le is good then it gives the better estimate for the dependent variable (TFR). Using new predictor variab le (proposed in this paper), the improved model exp lained about 85 percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational comp lexities at state level for different background characteristics. By using this modified estimate of TFR, the demographers can easily calculate the birth averted for different regions as well as states also.
{"title":"Estimation of Total Fertility Rate and Birth Averted due to Contraception: Regression Approach","authors":"K. K. Singh, Brijesh P Singh, Kushagra Gupta, S. Dst-Cim","doi":"10.5923/J.STATISTICS.20120205.01","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120205.01","url":null,"abstract":"Fert ility plays an important ro le in any demographic transition and total fert ility rate (TFR) is one of the basic measurements of fert ility. Due to non-availability of co mp lete and reliable data, a large nu mber of indirect techniques have been developed to estimate the demographic parameters with incomp lete data. So me of these techniques are based on utilizing the data fro m stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. In the present paper an indirect method has been proposed to estimate the TFR using regression analysis. In these types of analysis the most serious problem is the choice of predictor variable. If the choice of predictor variab le is good then it gives the better estimate for the dependent variable (TFR). Using new predictor variab le (proposed in this paper), the improved model exp lained about 85 percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational comp lexities at state level for different background characteristics. By using this modified estimate of TFR, the demographers can easily calculate the birth averted for different regions as well as states also.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"55 1","pages":"47-55"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88888351","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 : 2012-12-01DOI: 10.5923/J.STATISTICS.20120205.04
J. Subramani, G. Kumarapandiyan
In this paper we have proposed a class of modified ratio type variance estimators for estimation of population variance of the study variable using Quartiles and their functions of the auxiliary variable are known. The biases and mean squared errors of the proposed estimators are obtained and also derived the conditions for which the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators. Further we have compared the proposed estimators with that of traditional ratio type variance estimator and existing modified ratio type variance estimators for certain known populations. From the numerical study it is observed that the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators.
{"title":"Variance Estimation Using Quartiles and their Functions of an Auxiliary Variable","authors":"J. Subramani, G. Kumarapandiyan","doi":"10.5923/J.STATISTICS.20120205.04","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120205.04","url":null,"abstract":"In this paper we have proposed a class of modified ratio type variance estimators for estimation of population variance of the study variable using Quartiles and their functions of the auxiliary variable are known. The biases and mean squared errors of the proposed estimators are obtained and also derived the conditions for which the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators. Further we have compared the proposed estimators with that of traditional ratio type variance estimator and existing modified ratio type variance estimators for certain known populations. From the numerical study it is observed that the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"104 1","pages":"67-72"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80846384","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 : 2012-12-01DOI: 10.5923/J.STATISTICS.20120205.02
A. Adejumo, O. O. Sanni, E. Jolayemi, R. O. Ogedengbe
s In some categorical tables, one of the classifying variables may be at least ordinal (ran ked) arising fro m a follow-up o r any similar study. The other classifying variab le(s) may be that which separates the population into groups using variables such as gender, race or location, or a co mbination of some of them. The counts obtained this way are analyzed recognizing that one of the variables is nearly metric and must be used and interpretation becomes easier when appropriate model is fitted to the arising product multino mial. An examp le o f such an approach is provided using the data fro m Tuber- culosis Management in a Teaching Hospital. We observed that the recovery rate of females was faster than their males counterpart on the assumption that those discharged through management system follows an exponential distribution.
{"title":"Analysis of Categorical Panel Data","authors":"A. Adejumo, O. O. Sanni, E. Jolayemi, R. O. Ogedengbe","doi":"10.5923/J.STATISTICS.20120205.02","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120205.02","url":null,"abstract":"s In some categorical tables, one of the classifying variables may be at least ordinal (ran ked) arising fro m a follow-up o r any similar study. The other classifying variab le(s) may be that which separates the population into groups using variables such as gender, race or location, or a co mbination of some of them. The counts obtained this way are analyzed recognizing that one of the variables is nearly metric and must be used and interpretation becomes easier when appropriate model is fitted to the arising product multino mial. An examp le o f such an approach is provided using the data fro m Tuber- culosis Management in a Teaching Hospital. We observed that the recovery rate of females was faster than their males counterpart on the assumption that those discharged through management system follows an exponential distribution.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"27 1","pages":"56-59"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87243043","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 : 2012-12-01DOI: 10.5923/J.STATISTICS.20120205.05
N. Feroze, M. Aslam
In this paper, the prob lem o f estimating the scale parameter of log gamma distribution under Bayesian and maximu m likelihood framework has been addressed. The uniform and Jeffreys priors have been assumed for posterior analysis. The Bayes estimators and associated risks have been derived under five different loss functions. The credible intervals and highest posterior density intervals have been constructed under each prior. A simulat ion study has been carried out to illustrate the numerical applicat ions of the results and to compare the performance of different estimators. The purpose is to compare the performance of the estimators based on Bayesian and maximu m likelihood framewo rks. The performance of different Bayes estimators has also been compared using five d ifferent loss functions. The study indicated that for estimation of the said parameter, the Bayesian estimation can be preferred over maximu m likelihood estimation. While in case of the Bayesian estimation, the entropy loss function under Jeffreys can effectively be emp loyed.
{"title":"A Note on Bayesian and Maximum Likelihood Estimation of Scale Parameter of Log Gamma Distribution","authors":"N. Feroze, M. Aslam","doi":"10.5923/J.STATISTICS.20120205.05","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120205.05","url":null,"abstract":"In this paper, the prob lem o f estimating the scale parameter of log gamma distribution under Bayesian and maximu m likelihood framework has been addressed. The uniform and Jeffreys priors have been assumed for posterior analysis. The Bayes estimators and associated risks have been derived under five different loss functions. The credible intervals and highest posterior density intervals have been constructed under each prior. A simulat ion study has been carried out to illustrate the numerical applicat ions of the results and to compare the performance of different estimators. The purpose is to compare the performance of the estimators based on Bayesian and maximu m likelihood framewo rks. The performance of different Bayes estimators has also been compared using five d ifferent loss functions. The study indicated that for estimation of the said parameter, the Bayesian estimation can be preferred over maximu m likelihood estimation. While in case of the Bayesian estimation, the entropy loss function under Jeffreys can effectively be emp loyed.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"117 1","pages":"73-79"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80462182","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 : 2012-12-01DOI: 10.5923/J.STATISTICS.20120205.03
M. Kamal, S. Zarrin, S. Saxena, Arif-ul-Islam
In this paper the Weibull geo metric process model is utilized for the analysis of accelerated life testing under constant stress. By assuming that the lifetimes under increasing stress levels form a geometric process, the maximu m like lihood estimates of the parameters and their confidence intervals (CIs) using both asymptotic and parametric bootstrap method are derived. The performance of the estimators is evaluated by a simu lation study with different pre-fixed parameters. This paper also compares the geometric process model with the traditional log-linear model. A simulation study is also performed to co mpare the performances of the geometric model and the log-linear model.
{"title":"Weibull Geometric Process Model for the Analysis of Accelerated Life Testing with Complete Data","authors":"M. Kamal, S. Zarrin, S. Saxena, Arif-ul-Islam","doi":"10.5923/J.STATISTICS.20120205.03","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120205.03","url":null,"abstract":"In this paper the Weibull geo metric process model is utilized for the analysis of accelerated life testing under constant stress. By assuming that the lifetimes under increasing stress levels form a geometric process, the maximu m like lihood estimates of the parameters and their confidence intervals (CIs) using both asymptotic and parametric bootstrap method are derived. The performance of the estimators is evaluated by a simu lation study with different pre-fixed parameters. This paper also compares the geometric process model with the traditional log-linear model. A simulation study is also performed to co mpare the performances of the geometric model and the log-linear model.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"3 1","pages":"60-66"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81346266","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 : 2012-08-31DOI: 10.5923/J.STATISTICS.20110101.03
Özlem Ege Oruç, Armağan Kanca
A fundamental concept of information theory, relative entropy and mutual information, is directly applicable to evaluation of diagnostic test performance. The aim of this study is to demonstrate how basic concepts in information theory apply to the problem of quantifying major depressive disorder diagnostic test performance. In this study, the per- formances of the Dexamethasone Suppression Test-DST and the Thyroid-Stimulating Hormone Test-TSH, two of the di- agnosis tests of Major Depressive Disorder, are evaluated with the method of Information Theory. The amount of informa- tion gained by performing a diagnostic test can be quantified by calculating the relative entropy between the posttest and pretest probability distributions. And also demonstrates that diagnostic test performance can be quantified as the average amount of information the test result provides about the disease state. It is aimed that this study will hopefully give various points of view to the researchers who want to make research on this subject by explaining how the tests used for the diag- nosis of various diseases are evaluated with this way.
{"title":"Evaluation and Comparison of Diagnostic Test Performance Based on Information Theory","authors":"Özlem Ege Oruç, Armağan Kanca","doi":"10.5923/J.STATISTICS.20110101.03","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20110101.03","url":null,"abstract":"A fundamental concept of information theory, relative entropy and mutual information, is directly applicable to evaluation of diagnostic test performance. The aim of this study is to demonstrate how basic concepts in information theory apply to the problem of quantifying major depressive disorder diagnostic test performance. In this study, the per- formances of the Dexamethasone Suppression Test-DST and the Thyroid-Stimulating Hormone Test-TSH, two of the di- agnosis tests of Major Depressive Disorder, are evaluated with the method of Information Theory. The amount of informa- tion gained by performing a diagnostic test can be quantified by calculating the relative entropy between the posttest and pretest probability distributions. And also demonstrates that diagnostic test performance can be quantified as the average amount of information the test result provides about the disease state. It is aimed that this study will hopefully give various points of view to the researchers who want to make research on this subject by explaining how the tests used for the diag- nosis of various diseases are evaluated with this way.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"23 1","pages":"10-13"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77690057","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 : 2012-08-31DOI: 10.5923/J.STATISTICS.20120202.01
B. Hazra
This paper presents a new approach towards independent component analysis (ICA) for small samples of data, utilizing the linear combination of expectations of order statistics, also termed as L-moments. The main advantage of using L-moments is the relatively low bias in their estimation for small samples compared to the conventional moments. In the present work, arguments leading to kurtosis maximization ICA are first explored and a criterion based on the maximization of L-kurtosis is developed. The optimality criterion based on the extraction of a single source is then assessed. The independent components of the mixture are extracted sequentially using a deflationary approach. The quality of separation of independent components from a mixture is re-interpreted in terms of the distribution parameters of the recovered sources. The robustness of the proposed algorithm is demonstrated through simulation examples of separation of 2-source mixtures, a large-scale problem and a case study from health monitoring of civil structures.
{"title":"Independent Component Analysis Using Maximization of L-Kurtosis","authors":"B. Hazra","doi":"10.5923/J.STATISTICS.20120202.01","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120202.01","url":null,"abstract":"This paper presents a new approach towards independent component analysis (ICA) for small samples of data, utilizing the linear combination of expectations of order statistics, also termed as L-moments. The main advantage of using L-moments is the relatively low bias in their estimation for small samples compared to the conventional moments. In the present work, arguments leading to kurtosis maximization ICA are first explored and a criterion based on the maximization of L-kurtosis is developed. The optimality criterion based on the extraction of a single source is then assessed. The independent components of the mixture are extracted sequentially using a deflationary approach. The quality of separation of independent components from a mixture is re-interpreted in terms of the distribution parameters of the recovered sources. The robustness of the proposed algorithm is demonstrated through simulation examples of separation of 2-source mixtures, a large-scale problem and a case study from health monitoring of civil structures.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"36 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81200522","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 : 2012-08-31DOI: 10.5923/J.STATISTICS.20120203.01
M. S. El-Sherbeny, E. K. Al-Hussaini
In this paper, we investigate the reliability measures: availability () T A ∞ and mean time to system failure MTTF , for four configurations of series systems with mixed standby components: cold and warm. The time to repair and to failure for each of the operative and warm standby components are assumed to follow the exponential distribution. Com- parisons of the computed MTTF 's and steady state availabilities () T A ∞ for the four configurations are obtained for specific values of distribution parameters and cost of the components. The configurations are then ranked based on MTTF , () T A ∞ and cost/ benefit, where benefit is either MTTF or () T A ∞ . Asymptotic estimation of MTTF , () T A ∞ and cost/ benefit are computed for the optimal systems.
{"title":"Characteristic Reliability Measures of Mixed Standby Components and Asymptotic Estimation","authors":"M. S. El-Sherbeny, E. K. Al-Hussaini","doi":"10.5923/J.STATISTICS.20120203.01","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120203.01","url":null,"abstract":"In this paper, we investigate the reliability measures: availability () T A ∞ and mean time to system failure MTTF , for four configurations of series systems with mixed standby components: cold and warm. The time to repair and to failure for each of the operative and warm standby components are assumed to follow the exponential distribution. Com- parisons of the computed MTTF 's and steady state availabilities () T A ∞ for the four configurations are obtained for specific values of distribution parameters and cost of the components. The configurations are then ranked based on MTTF , () T A ∞ and cost/ benefit, where benefit is either MTTF or () T A ∞ . Asymptotic estimation of MTTF , () T A ∞ and cost/ benefit are computed for the optimal systems.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"20 1","pages":"11-23"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79200614","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 : 2012-08-31DOI: 10.5923/J.STATISTICS.20110101.02
A. Ammani
Over the years several authors have attributed the decline in Nigerian agricultural production to the neglect of the agricultural sector that resulted from the discovery of crude oil, what is known as the oilboom factor. This paper set out to find answer to the question: was agriculture really neglected as a result of the oilboom? The study took a historical per- spective to trace the path of capital expenditure allocations to the agricultural sector in Nigeria. Secondary data on planned capital expenditure allocation to the agriculture sector before and during the oilboom period; and the budget estimates of capital expenditure allocations to the Agriculture, Water Resources, Health, Education and Defence sectors in Nigeria during the oil boom period 1977-1983 were sourced and used. Graphic descriptive statistics and the one-way analysis of variance technique were used to achieve the objectives of the study. The Tukey's Multiple Comparison method w as employed to determine which mean(s) differ, in both cases, in the one-way analysis of variance tests conducted. The empirical findings of the study indicate significant increase in the quantity of capital expenditure allocation to the agriculture sector during the oilboom period; and that more capital expenditure was allocated to the agriculture sector than was allocated to either of Health, Education or Defence sectors in Nigeria during the oilboom period. Thus, it concluded that the decline in agricultural production in Nigeria was, statistically, not attributable to the neglect of the agricultural sector resulting from oil boom. The reason could be as a manifestation of Dutch Disease, Natural Resource Curse, Rent Seeking phenomenom, or something else.
{"title":"Nigeria’s Oilboom Period (1973-1983): Was Agriculture Really Neglected?","authors":"A. Ammani","doi":"10.5923/J.STATISTICS.20110101.02","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20110101.02","url":null,"abstract":"Over the years several authors have attributed the decline in Nigerian agricultural production to the neglect of the agricultural sector that resulted from the discovery of crude oil, what is known as the oilboom factor. This paper set out to find answer to the question: was agriculture really neglected as a result of the oilboom? The study took a historical per- spective to trace the path of capital expenditure allocations to the agricultural sector in Nigeria. Secondary data on planned capital expenditure allocation to the agriculture sector before and during the oilboom period; and the budget estimates of capital expenditure allocations to the Agriculture, Water Resources, Health, Education and Defence sectors in Nigeria during the oil boom period 1977-1983 were sourced and used. Graphic descriptive statistics and the one-way analysis of variance technique were used to achieve the objectives of the study. The Tukey's Multiple Comparison method w as employed to determine which mean(s) differ, in both cases, in the one-way analysis of variance tests conducted. The empirical findings of the study indicate significant increase in the quantity of capital expenditure allocation to the agriculture sector during the oilboom period; and that more capital expenditure was allocated to the agriculture sector than was allocated to either of Health, Education or Defence sectors in Nigeria during the oilboom period. Thus, it concluded that the decline in agricultural production in Nigeria was, statistically, not attributable to the neglect of the agricultural sector resulting from oil boom. The reason could be as a manifestation of Dutch Disease, Natural Resource Curse, Rent Seeking phenomenom, or something else.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"17 5","pages":"6-9"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72398743","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 : 2012-08-31DOI: 10.5923/J.STATISTICS.20110101.01
M. Islam
Diabetes mellitus is a set of diseases that involves troubles with the hormone insulin. It is characterized by chronic elevation of blood glucose level exceeding normal value. In this paper, an effort has been made to fit mathematical model to diabetic patients as well as its cumulative distribution for both sexes associated with age of Rajshahi City in Bangladesh. For this purpose, the data have been taken from Noor (2008). In this study, an attempt has been given attention to show that the polynomial model is tried to fit to the distribution of diabetic patients associated with age as well as its cu- mulative distribution. It is found that the distribution of diabetic patients for both sexes associated with age follows bi-quadratic polynomial model. Moreover, it is investigated that cumulative distribution of diabetic patients follow cubic polynomial model. Cross validity prediction power is employed to the fitted model to verify the stability of the model in this manuscript.
{"title":"Modeling of Diabetic Patients Associated with Age: Polynomial Model Approach","authors":"M. Islam","doi":"10.5923/J.STATISTICS.20110101.01","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20110101.01","url":null,"abstract":"Diabetes mellitus is a set of diseases that involves troubles with the hormone insulin. It is characterized by chronic elevation of blood glucose level exceeding normal value. In this paper, an effort has been made to fit mathematical model to diabetic patients as well as its cumulative distribution for both sexes associated with age of Rajshahi City in Bangladesh. For this purpose, the data have been taken from Noor (2008). In this study, an attempt has been given attention to show that the polynomial model is tried to fit to the distribution of diabetic patients associated with age as well as its cu- mulative distribution. It is found that the distribution of diabetic patients for both sexes associated with age follows bi-quadratic polynomial model. Moreover, it is investigated that cumulative distribution of diabetic patients follow cubic polynomial model. Cross validity prediction power is employed to the fitted model to verify the stability of the model in this manuscript.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"53 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82318623","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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