Browsing by Author "Kassahun, Taddesse"

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    A Production Function Analysis for Private Peasant Holdings Crop Farms in Ethiopia: An Application of Robust Regression
    (Addis Ababa University, 2009-06) Kassahun, Taddesse; Abegaz, Fentaw (PhD)
    This study applied production function analysis for private peasant holdings crop farms in Ethiopi a. Four major crop producing regions viz., Tigray, Amhara, Oromia and SNNP were included in the study. Three regression models for production fun ction namely, linear, exponential and Cobb Douglas were considered and thoroughly assessed {'or stati stical model diagnostics. The statistical model diagnostics and checking suggested that crop production function for each of the regions was found to be appropriately represented by the Cobb-Douglas production function based on data from the 2007/08 (2000 EC) agricultural sample survey. The Cobb-Douglas production function was first fitted for each region using ordinary least squares (OLS) regression. As expected, the parameter estimates using OLS were mi sleading due to the occurrence of several outlying cases and hence robust regression was taken as a viable alternative. Based on the results of robust regression, many of the parameter est imates took on the expected signs, the R2 values were substantially increased and the standard errors of parameter estimates were decreased at large. In general, robust regression results indicated that farm size, fe rtili zer, seed, oxen power and human labor were playing a pivotal role for the maximization of crop yield in each of the studied regions. From among these variables, the great contribution was found to be due to farm size in each of the regions with SNNP an exception in whi ch the great share was due to human labor. However, the contribution of education variable for crop yie ld was found to be stati sti cally insignificant and received negative sign in Tigray and Amhara regions. Counter to expectations, the coefficient estimate for irrigation variable in Tigray, Amhara and SNNP regions had come to obtain negative sign though it was not found to be stati stically significant. The product ion elasticities for each of the inputs at each region except farm size in Tigray, Amhara and Oromia suggested that the relation between inputs and output was inelastic, i.e., ho lding other {actors constant, the marginal return to each factor wi ll decrease as more of the factors are used. Additionally, crop production functions revealed that returns to scale were estim ated to be greater than unity in each of the regions indi cating increasing returns to scale.
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    Rank-Based Directional Test in k-Sample Multivariate Problems
    (Addis Ababa University, 2021-07-01) Kassahun, Taddesse; Wencheko, Eshetu (Professor); Bathke, Arne C. (Professor)
    Data from several response variables, potentially measured on different scales, occur naturally in various practical settings such as clinical trials. Treatment differences with respect to these response variables are usually analyzed using parametric methods under the assumptions of multivariate normality and covariance homogeneity. In many situations, however, these assumptions are not fulfilled, in particular when the response variables under consideration are ordered categorical. Thus, a rankbased (nonparametric) approach which disregards the above assumptions is desirable. More importantly, an investigator may be interested to test a global hypothesis of no treatment difference versus the alternative that treatment effects are monotonically increasing (decreasing) with respect to all responses. A number of studies have been conducted to address the issue of testing directional alternatives in two or more multivariate samples both in the parametric as well as nonparametric framework. Given that the response variables are measured in a mix of metric and ordered categorical scales, the parametric methods are not suitable. In turn, most of the contributions in the area of nonparametric statistics base their inferences on several pairwise comparisons of treatments in such a way that the ranks are being computed pairwise only, that is, only between those two levels that are compared at each step. This reduces the amount of available information and is well known to potentially lead to paradoxical situations. In order to incorporate more information from multivariate data for testing directional hypotheses which involve variables measured both in metric as well as ordered categorical scales in a unified manner we propose a new rank-based test statistic. The statistic we have derived is a multivariate generalization based on a coordinate-wise approach of a univariate test statistic proposed by Bathke (2009) for alternative patterns within a nonparametric framework. Separate ranking for different variables is employed in order to ensure invariance under monotone transformations of the responses as well as the weights describing alternative patters. Unlike most methods available in the literature, the newly introduced test handles data with ties, in particular, ordered categorical data as the underlying distribution is not required to be continuous. The test statistic introduced in this dissertation is proved to be accurate in detecting pre-specified equi-directional alternative patterns across two or more multivariate samples trough extensive simulation studies. A comparison is also made with that of the rank-sum type test for directional multivariate problems proposed by O’Brien (1984) in which the newly developed test is in par and sometimes better than the test by O’Brien. Applications to several datasets obtained from clinical trials are presented and potential extensions in different directions are discussed. The other more interesting practical issue in directional multivariate problems is to test conjectured alternative patterns in which treatments effects are monotonically increasing for some of the responses and monotonically decreasing for others. So long as treatment effects can be specified on a priori basis, we suggest interchanging the signs of different responses and making the anticipated direction of treatment effects similar. Following this, we employ the newly developed test statistic to test treatment effects in opposite directions in two or more multivariate samples. Furthermore, we employ the closed testing principle in conjunction with the test we have proposed in order to identify on which specific responses or sets of responses the effects are actually observed. An application of this procedure is demonstrated by re-analyzing a dose-response dataset. In summary, the test developed in this dissertation can handle monotone trends based on a complete case multivariate data. Developing a test statistic which can handle umbrella alternatives, and/or incomplete cases is differed to future research.