Ordinal logistic regression versus multiple binary logistic regression model for predicting student loan allocation

This paper examines two different methodologies to a classification problem of higher education loan applicants. The paper looks into the allocations made by the Higher Education Loans Board (HELB) relative to the economic status of the applicant. In this article, we modeled Higher Education Loans Board (HELB) loan application data from three public universities to determine whether the loan was allocated based on the needs of the respective applicants. The data was classified into two natural categories of those not allocated the loan (0) and those allocated the loan (1). This paper classified further to consider the amounts awarded by theHELB. This was possible since we observed that HELB loans were awarded indistinct categories (Kshs 0, Kshs 35,000, Kshs 40,000, Kshs 45,000, Kshs 50,000), Kshs 55,000 Kshs 60,000). In this study, we used ordinal logistic regression and multiple binary logistic regressions in classifying the applicants into the identified categories. The models were generated that included all predictor variables that were useful in predicting the response variable. This study found that HELB allocate a loan amount to Kshs 40,000 but anything behold Kshs 40,000 is based on information provided by an applicant. The study revealed that the loans were not awarded based on the need of respective applicants. This has led to misclassification when allocating loan. The study found that wealth and amount of fees paid for siblings were other factors that could be considered to identify needy applicants. This results show that an ordinal regression model gives  accurate estimates that can enable HELB make a viable awarding decision. It is expected that proper determination of the most accurate model will go a long way in minimizing the number of mis-classifications when awarding HELB loan. The study raises questions on the criteria used by HELB in loan allocation but further studies may be commissioned to confirm or  disapprove our findings. Key words: regression, logistic, binary, ordinal; higher education, loa

Parametric change point estimation, testing and confidence interval applied in business

In many applications like finance, industry and medicine, it is important to consider that the model parameters may undergo changes at unknown moment in time. This paper deals with estimation, testing and confidence interval of a change point for a univariate variable which is assumed to be normally distributed. To detect a possible change point, we use a Schwarz Information Criterion (SIC) statistic whose asymptotic distribution under the null hypothesis is determined. The percentile bootstrap method is used to construct the confidence interval of the estimated change point. The developed tools and methods are applied to the 1987 – 1988 US trade deficit data. Our results show that a significant change in US trade deficit occurred in November 1987. Further, it is shown that the percentile bootstrap confidence intervals are not always symmetrical.Key words: Change point, Schwarz information criterion, percentile bootstra

Imputation of incomplete non- stationary seasonal time series data

Missing observations in time series data is a common problem that occurs due to many reasons. In order to estimate missing observation accurately, it is necessary to select an appropriate model depending on the type and nature of the data being handled so as to obtain the best possible estimates of missing observations.  The objective of the study was to examine and compare the appropriateness of Box Jenkins models and direct linear regression in imputing missing observation in non stationary seasonal time series data. The study examined Box Jenkins techniques SARIMA and ARIMA models in imputing non stationary seasonal time series specifically in situations where missing observation are encountered towards the end of the series. Besides that,  direct linear regression have also been proposed in  imputing missing observations when seasonality has been relaxed by rearranging the time series data in periods and grouping observations which corresponds to each other from each period  together and then analyze each as a single series. From the study it was observed that it is easy to impute missing observations using direct linear regression in non-stationary time series data when seasonality has been relaxed by rearranging the data in periods compared to traditional Box Jenkins models SARIMA and ARIMA models. Also direct linear regression proved, more accurate and reliable compared to Box-Jenkins techniques. So Based on the finding, the proposed direct linear regression approach can be used in imputing missing observations for non stationary series with seasonality by first rearranging the data in periods. KEYWORDS: Imputation, SARIMA, ARIMA models and Direct Linear regression (L.REG)

Site characterization and systems analysis in Central Mekong

The systems addressed in this chapter and in the CGIAR Research Program on Integrated Systems for the Humid Tropics (Humidtropics) broadly include natural systems comprising biophysical, resource and climate realities; social systems made up of people, societies and their institutions; and, what some term as artificial systems built on elements of the first two (Checkland 1981). Agricultural systems, for example, modify natural systems for productive use, add infrastructure to provide markets, and modify human institutions to organize labour and services to enable the agricultural system to function. Regardless of how systems are categorized, they can be simplistically deconstructed into components and the interactions between them. In this chapter we characterize some of the Central Mekong systems, and also address some of the system dynamics, at two basic levels of resolution. Section 2 addresses regional agricultural systems consisting of one or more districts within a country, and includes variations in natural and social systems in addition to agricultural systems. Five regional cases that reflect the diversity across the Central Mekong Action Area are examined and compared. The authors focus on systems at the community or local landscape level, particularly the individual farm household component, and the variation between households within the landscape. Variables include household agricultural practices, household resources, capacity, and links to markets and institutions. Section 3 looks at diversity in the variables among farm households and the implications for livelihoods and well-being. Section 4 examines food security levels arising from specific farm household strategies and performance, how the two are related, and the implications for potential farm interventions. We conclude by comparing the types of systems examined, the differences in types of tools needed, and the differences in questions asked and learning generated. Throughout this chapter, authors refer to data from reports and articles that interested readers can find in Annex I

Seasonal time series data imputation: Comparison between feed forward neural networks and parametric approaches

Missing data in a time series may be an obstacle that may prevents further analysis of the available series either for control, explanations or forecasting. This paper addresses the problem of "‘filling in"' missing data is a segment of seasonal univariate time series in order for further analysis to be possible. We focus on extrapolation from models fitted to available segments using both parametric and non parametric methods. Specifically we examine how recursive and direct estimates from forward and backward learning Artificial Neural Networks (ANN) compares with seasonal ARIMA estimates and interpolation estimates of Additive outliers in seasonal ARIMA models. A comparison statistics is also proposed. Keywords: Time Series; Artificial Neural Network (ANN); Direct estimates; Recursive estimates; Outliers; Proximity > East African Journal of Statistics Vol. 1 (1) 2005: pp. 68-8

Politics in Kenya 1. The political culture of Kenya; 2. Politics and democracy in Kenya

Comprises 2 separate papersSIGLEAvailable from British Library Document Supply Centre- DSC:6217.2385(EU-CAS-OP--37) / BLDSC - British Library Document Supply CentreGBUnited Kingdo