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Small Area Estimation of Poverty Incidence with Sampling Error Variances Through Generalized Variance Function
Issue:
Volume 6, Issue 2, March 2017
Pages:
72-78
Received:
30 December 2016
Accepted:
12 January 2017
Published:
27 February 2017
Abstract: Small area estimation based on area level models, particularly the EBLUP method, typically assumes that sampling error variances of the direct survey small area estimates are known. In practice, the sampling error variances are unknown. This paper generates EBLUP estimates of poverty incidence when the sampling error variances are estimated using the generalized variance function (GVF) approach. The precision of the EBLUP estimates is determined using a modified version of the Prasad-Rao MSPE estimator. The modification is made by adding an extra term that would account the uncertainty associated with estimating the sampling error variances. The performance of the modified Prasad-Rao estimator relative to the commonly used Prasad-Rao estimator is evaluated through a simulation study. Results have shown that the modified Prasad-Rao MSPE estimator has relatively greater bias than the commonly used Prasad-Rao MSPE estimator, particularly for small samples. A slight gain in precision is observed when using the modified PR MSPE estimator, especially for large samples. Moreover, the findings imply that estimating sampling error variances using GVF models can be a very useful strategy in the application of EBLUP small area estimation, most particularly in poverty incidence estimation.
Abstract: Small area estimation based on area level models, particularly the EBLUP method, typically assumes that sampling error variances of the direct survey small area estimates are known. In practice, the sampling error variances are unknown. This paper generates EBLUP estimates of poverty incidence when the sampling error variances are estimated using t...
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Sequentially Selecting Between Two Experiment for Optimal Estimation of a Trait with Misclassification
George Matiri,
Kennedy Nyongesa,
Ali Islam
Issue:
Volume 6, Issue 2, March 2017
Pages:
79-89
Received:
18 January 2017
Accepted:
3 February 2017
Published:
27 February 2017
Abstract: The idea of pool testing originated with Dorfman during the World War II as an economical method of testing blood samples of army inductees in order to detect the presence of infection. Dorfman proposed that rather than testing each blood sample individually, portions of each of the samples can be pooled and the pooled sample tested first. If the pooled sample is free of infection, all inductees in the pooled sample are passed with no further tests otherwise the remaining portions of each of the blood samples are tested individually. Apart from classification problem, pool testing can also be used in estimating the prevalence rate of a trait in a population which was the focus of our study. In approximating the prevalence rate, one-at-a-time testing is time consuming, non-cost effective and is bound to errors hence pool testing procedures have been proposed to address these problems. This study has developed statistical model which is used to sequentially switching between two experiments when the sensitivity and specificity of the test kits is less than 100%. The experiments are selected sequentially, so that at each stage, the information available at that stage is used to determine which experiment to carry out at the next stage. The method of maximum likelihood estimator (MLE) was used in obtaining the estimators. The fisher information of different experiments is compared and the cut off values where one experiment is better than the other are calculated. The variance of the estimators has also been compared. The joint model has been compared to one-at-a-time and pool testing models by computing ARE. The joint model is found to be more efficient.
Abstract: The idea of pool testing originated with Dorfman during the World War II as an economical method of testing blood samples of army inductees in order to detect the presence of infection. Dorfman proposed that rather than testing each blood sample individually, portions of each of the samples can be pooled and the pooled sample tested first. If the p...
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Scale Independent Principal Component Analysis and Factor Analysis with Preserved Inherent Variability of the Indicators
Priyadarshana Dharmawardena,
Raphel Ouseph Thattil,
Sembakutti Samita
Issue:
Volume 6, Issue 2, March 2017
Pages:
90-94
Received:
2 February 2017
Accepted:
17 February 2017
Published:
2 March 2017
Abstract: Principal Component Analysis (PCA) and Factor Analysis (FA) are common multivariate techniques used for dimensionality reduction. With these techniques it is expected to identify actual number of dimensions while accounting almost all observed variability. Standard PCA is based either on correlation matrix (CORM) or covariance matrix (COVM). When it is based on CORM, scale dependency can be removed but inherent variability cannot be preserved. On the other hand, when PCA is based on COVM, inherent variability can be preserved but scale dependency cannot be removed. As a solution to this issue, this paper suggests scaling each indicator by its mean, resulting in new mean equal to 1 and standard deviation equal to the coefficient of variance (CV). This leads to PCs, which are scale independent while retaining the observed variability. The computation of PCs and factors under the suggested method is derived in the study. The procedure is illustrated using the lowest level administrative division census data of Western province of Sri Lanka.
Abstract: Principal Component Analysis (PCA) and Factor Analysis (FA) are common multivariate techniques used for dimensionality reduction. With these techniques it is expected to identify actual number of dimensions while accounting almost all observed variability. Standard PCA is based either on correlation matrix (CORM) or covariance matrix (COVM). When i...
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Comparative Analysis on EWMA and Poisson Cusum Chart in the Assessment of Road Traffic Crashes (RTC) in Osun State Nigeria
Faweya Olanrewaju,
Ogunwale Olukunle Daniel,
Adeniran Adefemi Tajudeen
Issue:
Volume 6, Issue 2, March 2017
Pages:
95-99
Received:
14 October 2016
Accepted:
31 January 2017
Published:
10 March 2017
Abstract: This study examines the daily Road Traffic Crashes (RTC) casualties in Osun state of Nigeria, Comparison of two quality control schemes identified as exponential Weighted Moving Average (EWMA) and Poisson Cumulative sum (CUSUM) control chart techniques were made using data collected from Federal Road Safety Corps (FRSC) Osun-State over a period 120 months our findings revealed that both techniques are capable of detecting small shift from mean level but Cusum is more sensitive. Furthermore, the points of change in the process are clearly identified and easily located on both schemes. The findings provide a proper diagnostic solution in the area of Road Traffic crash (RTC) reduction and control in Osun state Nigeria.
Abstract: This study examines the daily Road Traffic Crashes (RTC) casualties in Osun state of Nigeria, Comparison of two quality control schemes identified as exponential Weighted Moving Average (EWMA) and Poisson Cumulative sum (CUSUM) control chart techniques were made using data collected from Federal Road Safety Corps (FRSC) Osun-State over a period 120...
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The Impact of Demographic and Risk Behavioural Factors on the Prevalence of HIV/AIDS in Butajira, Ethiopia
Kassaye Wudu Seid,
Yoseph Tadesse Dessie
Issue:
Volume 6, Issue 2, March 2017
Pages:
100-107
Received:
12 November 2016
Accepted:
27 February 2017
Published:
18 March 2017
Abstract: The study aims at describing the impacts of some demographic and risk behavior factors. The data for this study were taken from Butajira Hospital and data were analyzed using SPSS. In this study, chi square test of association is employed to test the association between determinant factors and the prevalence of HIV/AIDS in Butajira, Ethiopia. The result indicates all the proposed variables have significant association with prevalence of HIV/AIDS. Finally a binary logistic regression model is adopted for the analysis of the impact of demographic and HIV related risk factors on client’s status with HIV/AIDS. The result revealed that, client’s age, marital status, education level, occupation, suspected exposure time and condom use found to be significant at 5% significance level, indicating strong effects on prevalence of HIV/AIDS. The probability that an adult in this age groups 24-31, 32-45 and more than 46 years are 1.26 times (odds=1.26), 2.864 times (odds=2.864) 3.945 times (odds=3.945) more likely than those individuals aged between 16 and 23. Educational levels of clients have a significant influence on HIV infection (p value <0.05), as the result individuals who enrolled in secondary education level are more affected than those other individuals (i.e. Odds =1.227). It was also observed that those clients who had no sexual practices in the past are less likely to be infected by HIV/AIDS than those clients had sexual practice before (odds 0.453).
Abstract: The study aims at describing the impacts of some demographic and risk behavior factors. The data for this study were taken from Butajira Hospital and data were analyzed using SPSS. In this study, chi square test of association is employed to test the association between determinant factors and the prevalence of HIV/AIDS in Butajira, Ethiopia. The r...
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Application of Central Composite Design Based Response Surface Methodology in Parameter Optimization of Watermelon Fruit Weight Using Organic Manure
Dennis K. Muriithi,
J. K. Arap Koske,
Geofrey K. Gathungu
Issue:
Volume 6, Issue 2, March 2017
Pages:
108-116
Received:
17 January 2017
Accepted:
8 February 2017
Published:
18 March 2017
Abstract: Response Surface Methodology (RSM) is a critical technology in developing new processes, optimizing their performance and improving the design. In Kenya, watermelon cultivation is gradually gaining ground. It is a crop with huge economic importance to man as well as highly nutritious, sweet and thirst- quenching. In order to increase crop production, there is need to increase soil nutrient content with organic manure such as poultry, cow or other animal wastes. At present, there are no recommended standards with respect to rate of poultry manure, cow manure and goat manure for enhancement of yield of watermelon in Kenya. The main objective of the study was to develop an approach for better understanding of the relationship between variables and response for optimum operating settings for maximum yield of watermelon crop using Central Composite Design and Response Surface Methodology. Response Surface Model evolved for response shown the effect of each input parameter and its interaction with other parameters, depicting the trend of response. Verification of the Fitness of the model using ANOVA technique shows that the model can be used with confidence level of 0.95, for watermelon production. Further validation of the model done with the additional experimental data collected demonstrates that the model have high reliability for adoption within the chosen range of parameters. The optimal value for each factor was found as 17.13tons/Ha of poultry manure, 13.3tons/Ha of cow manure and 18.1tons/Ha of goat manure. At optimal conditions, the actual value of the fruit weight of watermelon was 93.148tons/Ha. This translates to 37.3tons per acre piece of land of watermelon fruit weight for a period of 75-85 days after sowing. In addition, a peasant farmer can generate about 745,184 Kenya shillings within a period of 75 day in one acre piece of land at a low price of Kshs 20 per kilogram of watermelon fruit. RSM has resulted in saving of considerable amount of time and money hence recommended in similar study.
Abstract: Response Surface Methodology (RSM) is a critical technology in developing new processes, optimizing their performance and improving the design. In Kenya, watermelon cultivation is gradually gaining ground. It is a crop with huge economic importance to man as well as highly nutritious, sweet and thirst- quenching. In order to increase crop productio...
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On Bootstrap Confidence Intervals Associated with Nonparametric Regression Estimators for A Finite Population Total
Nicholas Makumi,
Romanus Odhiambo,
George Otieno Orwa,
Stellamaris Adhiambo
Issue:
Volume 6, Issue 2, March 2017
Pages:
117-122
Received:
17 February 2017
Accepted:
1 March 2017
Published:
21 March 2017
Abstract: The precision of an estimator is at times discussed regarding the variance. Usually, the exact value of the variance is unknown. The discussion relies on unknown populace quantities. When a researcher obtains the survey data, an estimate of the variance can, therefore, be calculated. When survey results are presented, it is good practice to provide variance estimates for the estimator used in the study. The estimator of the variance can further be used to construct confidence interval, assuming that the sampling distribution of estimator is approximately normal. This study proposes estimation of standard error and confidence interval for a nonparametric regression estimator for a finite population using bootstrapping method. The idea behind bootstrapping is to carry out computations on the collected data. Computation activity assists in estimating the disparity of statistics that are themselves computed from the same data. The variance of the Nadaraya-Watson estimator is derived, based on bootstrap procedure. This operation has led to the derivation of confidence interval associated with Nadaraya-Watson estimator of the population total. A simulation study has been carried out. The overall conclusion is that the confidence interval associated with Nadaraya-Watson estimator is tighter than all the other estimators (Horvitz-Thompson estimator, Local linear estimator, and Ratio estimator).
Abstract: The precision of an estimator is at times discussed regarding the variance. Usually, the exact value of the variance is unknown. The discussion relies on unknown populace quantities. When a researcher obtains the survey data, an estimate of the variance can, therefore, be calculated. When survey results are presented, it is good practice to provide...
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Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya
Kinyua Ann Muthoni,
Oscar Owino Ngesa
Issue:
Volume 6, Issue 2, March 2017
Pages:
123-128
Received:
20 February 2017
Accepted:
1 March 2017
Published:
21 March 2017
Abstract: Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya using data from the 2014 Kenya Demographic and Health Survey (KDHS). Four models were used in this study namely the logistic regression model, the normal unstructured heterogeneity random effects model, ICAR (Integrated Conditional Autoregressive) spatial random effects model and the convolution model. A full Bayesian approach was used and the models were implemented using the Winbugs software version 1.4. Model selection was based on the DIC value where the model with the lowest DIC value was considered to be the best. The convolution model was the best model in this case and was used to map ARI across the different counties in Kenya. The national prevalence was 47.3%. The prevalence was found to be highest in the counties in the western part of Kenya. From the analysis, it’s clear that ARI is still a menace that need to be controlled. Proper planning and allocation of resources need to be put in place by the county governments in order to curb the rising cases of ARI.
Abstract: Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya u...
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