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Search Results: 1 - 10 of 111928 matches for " Romanus O. Odhiambo "
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Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach  [PDF]
Charles K. Syengo, Sarah Pyeye, George O. Orwa, Romanus O. Odhiambo
Open Journal of Statistics (OJS) , 2016, DOI: 10.4236/ojs.2016.66088
Abstract: In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.
Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression  [PDF]
Sarah Pyeye, Charles K. Syengo, Leo Odongo, George O. Orwa, Romanus O. Odhiambo
Open Journal of Statistics (OJS) , 2016, DOI: 10.4236/ojs.2016.66092
Abstract: The study focuses on the imputation for the longitudinal survey data which often has nonignorable nonrespondents. Local linear regression is used to impute the missing values and then the estimation of the time-dependent finite populations means. The asymptotic properties (unbiasedness and consistency) of the proposed estimator are investigated. Comparisons between different parametric and nonparametric estimators are performed based on the bootstrap standard deviation, mean square error and percentage relative bias. A simulation study is carried out to determine the best performing estimator of the time-dependent finite population means. The simulation results show that local linear regression estimator yields good properties.
Estimation of Population Variance Using the Coefficient of Kurtosis and Median of an Auxiliary Variable under Simple Random Sampling  [PDF]
Tonui Kiplangat Milton, Romanus Otieno Odhiambo, George Otieno Orwa
Open Journal of Statistics (OJS) , 2017, DOI: 10.4236/ojs.2017.76066

In this study we have proposed a modified ratio type estimator for population variance of the study variable y under simple random sampling without replacement making use of coefficient of kurtosis and median of an auxiliary variable x. The estimator’s properties have been derived up to first order of Taylor’s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. Empirical studies have been done using real populations to demonstrate the performance of the developed estimator in comparison with the existing estimators. The proposed estimator as illustrated by the empirical studies performs better than the existing estimators under some specified conditions i.e. it has the smallest Mean Squared Error and the highest Percentage Relative Efficiency. The developed estimator therefore is suitable to be applied to situations in which the variable of interest has a positive correlation with the auxiliary variable.

A Multiplicative Bias Correction for Nonparametric Approach and the Two Sample Problem in Sample Survey  [PDF]
Kemtim Tamboun Stephane, Romanus Odhiambo Otieno, Thomas Mageto
Open Journal of Statistics (OJS) , 2017, DOI: 10.4236/ojs.2017.76073
Abstract: Let two separate surveys collect related information on a single population U. Consider situation where we want to best combine data from the two surveys to yield a single set of estimates of a population quantity (population parameter) of interest. This Article presents a multiplicative bias reduction estimator for nonparametric regression to two sample problem in sample survey. The approach consists to apply a multiplicative bias correction to an estimator. The multiplicative bias correction method which was proposed, by Linton & Nielsen, 1994, assures a positive estimate and reduces the bias of the estimate with negligible increase in variance. Even as we apply this method to the two sample problem in sample survey, we found out through the study of it asymptotic properties that it was asymptotically unbiased, and statistically consistent. Furthermore an empirical study was carried out to compare the performance of the developed estimator with the existing ones.
Robust estimation of variance in the presence of nearest neighbour imputation
Charles Wafula, Romanus Odhiambo Otieno, Mugo Maxwell Mwenda
African Journal of Science and Technology , 2003,
Abstract: The problem of estimating the variance of an estimator of the population total when missing values have been filled using a Nearest Neighbour (NN) imputation method is considered. The estimator is developed assuming a more general model than those considered in earlier studies. In an empirical study involving two artificial populations, the proposed estimator is found to perform better or as well as other two estimators in the current use. African Journal of Science and Technology Vol.4(2) 2003: 5-11
George Otieno Orwa,Romanus Odhiambo Otieno,Peter Nyamuhanga Mwita
Pakistan Journal of Statistics and Operation Research , 2010, DOI: 10.1234/pjsor.v6i1.149
Abstract: We propose a nonparametric regression approach to the estimation of a finite population total in model based frameworks in the case of stratified sampling. Similar work has been done, by Nadaraya and Watson (1964), Hansen et al (1983), and Breidt and Opsomer (2000). Our point of departure from these works is at selection of the sampling weights within every stratum, where we treat the individual strata as compact Abelian groups and demonstrate that the resulting proposed estimator is easier to compute. We also make use of mixed ratios but this time not in the contexts of simple random sampling or two stage cluster sampling, but in stratified sampling schemes, where a void still exists.
Christopher Ouma Onyango,Romanus Odhiambo Otieno,George Otieno Orwa
Pakistan Journal of Statistics and Operation Research , 2010, DOI: 10.1234/pjsor.v6i2.128
Abstract: Chambers and Dorfman (2002) constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008) have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.
Spatial-Temporal Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Growing Hierarchical Self-Organizing Maps  [PDF]
John W. Makokha, Jared O. Odhiambo
Journal of Geoscience and Environment Protection (GEP) , 2018, DOI: 10.4236/gep.2018.66008
Abstract: Neural network analysis based on Growing Hierarchical Self-Organizing Map (GHSOM) is used to examine Spatial-Temporal characteristics in Aerosol Optical Depth (AOD), Ångström Exponent (ÅE) and Precipitation Rate (PR) over selected East African sites from 2000 to 2014. The selected sites of study are Nairobi (1°S, 36°E), Mbita (0°S, 34°E), Mau Forest (0.0° - 0.6°S; 35.1°E - 35.7°E), Malindi (2°S, 40°E), Mount Kilimanjaro (3°S, 37°E) and Kampala (0°N, 32.1°E). GHSOM analysis reveals a marked spatial variability in AOD and ÅE that is associated to changing PR, urban heat islands, diffusion, direct emission, hygroscopic growth and their scavenging from the atmosphere specific to each site. Furthermore, spatial variability in AOD, ÅE and PR is distinct since each variable corresponds to a unique level of classification. On the other hand, GHSOM algorithm efficiently discriminated by means of clustering between AOD, ÅE and PR during Long and Short rain spells and dry spell over each variable emphasizing their temporal evolution. The utilization of GHSOM therefore confirms the fact that regional aerosol characteristics are highly variable be it spatially or temporally and as well modulated by PR received over each variable.
A Mathematical Modelling of the Effect of Treatment in the Control of Malaria in a Population with Infected Immigrants  [PDF]
Olaniyi S. Maliki, Ngwu Romanus, Bruno O. Onyemegbulem
Applied Mathematics (AM) , 2018, DOI: 10.4236/am.2018.911081
In this work, we developed a compartmental bio-mathematical model to study the effect of treatment in the control of malaria in a population with infected immigrants. In particular, the vector-host population model consists of eleven variables, for which graphical profiles were provided to depict their individual variations with time. This was possible with the help of MathCAD software which implements the Runge-Kutta numerical algorithm to solve numerically the eleven differential equations representing the vector-host malaria population model. We computed the basic reproduction ratio R0 following the next generation matrix. This procedure converts a system of ordinary differential equations of a model of infectious disease dynamics to an operator that translates from one generation of infectious individuals to the next. We obtained R0 = \"\", i.e., the square root of the product of the basic reproduction ratios for the mosquito and human populations respectively. R0m explains the number of humans that one mosquito can infect through contact during the life time it survives as infectious. R0h on the other hand describes the number of mosquitoes that are infected through contacts with the infectious human during infectious period. Sensitivity analysis was performed for the parameters of the model to help us know which parameters in particular have high impact on the disease transmission, in other words on the basic reproduction ratio R0.
Peter Nyamuhanga Mwita,Romanus Odhiambo Otieno,Verdiana Grace Masanja,Charles Muyanja
Pakistan Journal of Statistics and Operation Research , 2011, DOI: 10.1234/pjsor.v7i2.241
Abstract: This paper considers the modeling and prediction of households food security status using a sample of households in the Lake Victoria region of Kenya. A priori expected food security factors and their measurements are given. A binary logistic regression model derived was fitted to thirteen priori expected factors. Analysis of the marginal effects revealed that effecting the use of the seven significant determinants: farmland size, per capita aggregate production, household size, gender of household head, use of fertilizer, use of pesticide/herbicide and education of household head, increase the likelihood of a household being food secure. Finally, interpretations of predicted conditional probabilities, following improvement of significant determinants, are given.
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