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.

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.

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.

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 problemin
sample survey, we found out through the study of it asymptotic properties that
it wasasymptotically unbiased, and statistically consistent. Furthermore an
empirical study wascarried out to compare the performance of the developed estimator with
the existing ones.

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

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.

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.

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.

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 R_{0} 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 R_{0} = , i.e., the square root of the product of
the basic reproduction ratios for the mosquito and human populations respectively. R_{0m} explains the number of humans that one mosquito can infect
through contact during the life time it survives as infectious. R_{0h} 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 R_{0}.

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.