F. W. O. Aduol

AN EVALUATION OF SOME ROBUST ESTIMATION TECHNIQUES IN THE ESTIMATION OF GEODETIC PARAMETERS

Abstract In Part 1 of this study, we reported on an evaluation of the performances of four robust estimation techniques, namely: the modified residuals technique, the modified weights technique, the modified residuals-weights technique, and the iterative weighting technique. The methods were then tested on a simulated geodetic network. In this second part of the study, we carry out a further evaluation of the methods with a real geodetic network as the test network. In order that a set of gross-error contaminated observational may be available for the study, gross errors have been simulated into observational set. The results of the study largely corroborate the findings in Part 1 of the study.

A model for estimating student unit cost and staffing requirements for university academic programmes with reference to Kenyan public universities

A model for the estimation of student unit costs and the staffing requirements for university academic programmes is presented. The development of the model starts off with the specification of a staff distribution matrix, which sets out the proportions of the various staff levels in a given staff category that are needed to service at a particular degree level. The categories of staff considered are teaching (academic), senior administrative, technical, clerical, and semi-skilled. Within a given category of staff are considered various staff levels, e.g. Professor, Senior Lecturer and Lecturer in the case of the teaching staff category. The academic programmes are considered to be taken at the Bachelors, Masters and the Doctorate degree levels. Ratios between numbers of staff in a given level within a category, as well as ratios of staff within the category needed to service at the various degree levels are specified a priori. Academic (teaching) student-staff ratios for the various programmes are also set out a priori. Student-staff ratios for the other categories of staff are then computationally derived from the academic student-staff ratios. For each staff category a staff distribution matrix is then worked out. With the staff distribution matrix thus specified, the student unit cost and staffing requirement for a given academic programme are computed through various manipulations on the matrix. As a test example, the model is used to estimate student unit cost and staffing requirements for the six public universities in Kenya.

Robust Geodetic Parameter Estimation Under Least Squares Through Weighting on the Basis of the Mean Square Error

A technique for the robust estimation of geodetic parameters under the least squares method when weights are specified through the use of the mean square error is presented. The mean square error is considered in the specification of observational weights instead of the conventional approach based on the observational variance. The practical application of the proposed approach is demonstrated through computational examples based on a geodetic network. The results indicate that the least squares estimation with observational weights based on the mean square error is relatively robust against outliers in the observational set, provided the network (or the system) under consideration has a good level of reliability, as to make the network (or system) stable under estimation.

TRANSFORMATION BETWEEN GPS COORDINATES AND LOCAL PLANE UTM COORDINATES USING THE EXCEL SPREADSHEET

Abstract A procedure for obtaining plane UTM grid coordinates from GPS coordinates based on WGS84 reference system by use of Excel spreadsheet is outlined The geographical coordinates obtained from GPS observations are transfornted to the local reference system by use of a seven-parameter transformation. The local geographical coordinates are then transformed to plane UTM grid coordinates. Thereafter, the reverse computation is also performed to act as a check, i.e. transformation from grid coordinates to geographical coordinates.