In this work we have made use of a geometric approach which quantifies robustness and performance and we finally combine them using a cost function. In particular, we calculate the robustness of the estimate of standard deviation of nominally Laplacian distribution. As this distribution is imperfectly known, we employ a more general family, the generalized Gaussian; Laplacian distribution, is one of the members of this family. We compute parameter estimates and present a classical algorithm which is then analyzed for distribution from the generalized Gaussian family. We calculate the mean squared error according to the censoring height k. We measure performance as a function of (1/MSE) and combine it with robustness using a cost criterion and design a robust estimator which optimizes a mix of performance and robustness specified by the user.

The need for improving cost estimation for solid waste management (SWM) is particularly strong in emerging (industrialising) economies where problems of solid waste are severe, expectations for improvements are high, but finances are constrained. Using literature-based evaluation, traditional methods used to estimate costs of SWM in industrialising regions are classified into two categories- the unit cost method and benchmarking techniques. These current approaches are unable to satisfy two important SWM objectives in industrialising regions- 1. provide an understanding of variables affecting costs of SWM in developing countries, which in turn helps in developing a sound financing strategy, and 2. ensure that scarce financial resources are used to best effect while planning for increasing populations, and raising service levels in developing countries The development of cost models using the cost function approach, which are sometimes used as a cost estimation technique for developed countries' waste management case studies, is deemed as an improvement over current cost estimation approaches for SWM used in industrialising regions. The usefulness, applications and limitations of the cost function approach for developing countries is shown in four ways. First, the application of the cost function methodology to a developing country dataset is shown using data from approximately 300 Indian municipalities. Second, future developmental scenario analyses is conducted at the city level to estimate marginal costs to improve solid waste management (SWM) to handle increasing populations and to raise the level of service. Third, the basic intents for conducting cost function analyses are categorised based on the rich experiences from another public service with many similar characteristics, namely the healthcare sector, and translated for easy understanding for future solid waste engineers. Finally, the potential implications of the health care analyses on the developing city case study demonstrates the way forward in terms of the most important data that needs to be collected and future cost analyses that needs to be conducted. The results from this work indicate a strong need for careful selection and management of data, and awareness of the challenges that developing country datasets pose. The thesis is designed to encourage planners in developing countries to ditch heuristic thinking when planning improvements to SWM, and instead adopt modern rational methods to make cost-wise decisions. Specifically, this thesis provides solid waste management analysts the necessary tools to gather, analyse and interpret cost information in a way that facilitates planning of restricted finances in industrialising regions.

No description available.

This study applies a test for subadditivity (natural monopoly) to Australian telecommunications industry data for the period 1954 to 1990. If an industry exhibits subadditive cost, a monopoly can provide total industry output at a lower cost than multiple firms. The test for subadditivity is dependent on econometric estimation of a theoretically valid cost function. The cost function employed in this study is a multiple output variation of the symmetric generalised McFadden cost function. The main advantage of this specification is the ability to impose concavity on the cost function with respect to the input prices without imposing a priori restrictions on the input substitution elasticities.While there have been numerous previous subadditivity studies, this study is novel in two respects. First, this study contains the results of a direct test involving the provision of data carriage services provided by Australias monopoly carrier from 1970 to 1990. Thus, the test for subadditivity is applied to a relatively new service at a time when demand is in its infancy. Second, the approach to modelling makes explicit allowance for radical technological changes and lags in adjustment.The results indicate cost complementarity between data-aggregate output and large economies of scale. However, these effects are not strong enough to guarantee subadditivity. Analysis suggests that the most likely cause of subadditivity is the extent of network duplication between competitors. Evidence of subadditivity is found for firms that duplicate more than 30% of the networks fixed cost. This implies that at the national level, competition policy is the right choice. This suggests that regulated competition is likely to be no more costly than monopoly.

This paper provides a cost-efficient business mix and principles of regulation for the Kaohsiung Harbor Bureau. The authors use a multi-product model that represents port operations with three variables: mooring, stevedoring, and warehousing. A Fourier cost function is used to calculate several cost indicators. The sample covers monthly data in 1997-2001. By analyzing the cost indicators obtained, it is found that both individual operations and all of the operations together as a whole exhibit significant economies of scale and scope. Moreover, the presence of excess capacity in stevedoring and warehousing have been detected, as well as a presence of cost complement between mooring and stevedoring. Port operations cannot improve economic efficiency with perfect competition. On the contrary, the government should design economically reasonable regulations. Because port operations can not improve economic efficiency by market mechanism, port operations should be regulated by a proper authority in order to effectuate maximum social welfare.

The copper smelting industry has under gone extreme change over the past three decades. These changes have reordered dramatically the demand for inputs and the way in which those inputs have been utilized. The stimulus for change has come from multiple sources, and chief among these stimuli has been the mandate to sharply curtail the atmospheric release of sulfur dioxide. Even though the total emissions were lower than those from steam generation of electricity and from the refinery and petro-chemical industry, the perceived local and regional impact of sulfur dioxide forced extreme changes in the utilization of fundamental inputs of capital, labor, energy and materials.This study attempts to analyze these input use changes by modeling the industry as a translog cost function and by generating a number of associated elasticities. In addition to the four basic inputs, the model includes as control variables output, and other variables that represent pollution abatement and technical change.The challenge of estimating a large model on a limited number of observations has delivered information that is more limited in scope than was originally desired. The proxy for technical change did not produce significant parameters and the pollution abatement proxy is limited in its participation in the results. The range of elasticities computed reveal a picture of an industry characterized by inelasticity, in general, labor and energy being part of the exceptions. The industry is found to be sensitive to output level in its degree of elasticity among inputs.The translog model is found to be an effective tool for industry analysis. The promise of detailed analytical information may be even greater at the firm level where data are more accurate and the number of observations far greater.

No description available.

<p> The very sophisticated control systems of today are built around computers. It is felt that an improved form of cost function in vector or matrix form is needed to fully and most easily utilize the computer's advantages. After defining a vector cost function G , the problem of adapting and learning simplifies to the solution of a partial difference equation. Total system properties are easily defined as matrix arrays which are "learned" in an adapting and "learning" control loop.</p> <p> The relative merits of open and closed loop adaptive systems were investigated. The Nth order adaptive control system was finally chosen to be closed loop after developing two criterion equations in two unknowns which, if satisfied guaranteed improved system sensitivity with the closed loop configuration.</p> <p> Finally, several simple examples are given in experiment form to demonstrate the applicability of the proposed control system techniques.</p> / Thesis / Master of Engineering (MEngr)

Doctor of Philosophy / Department of Agricultural Economics / Jeffrey M. Peterson / This study focused on the economic efficiency of Kansas farms. The goal was to investigate factors and how they might affect farms and their economic and production performance. Kansas was selected as the region of study for its large agricultural production and distinctive type of multiple-operation farms. Farms in the sample could produce three outputs, crops, livestock and custom work. Inputs for the farms included measures of capital, labor, land and purchased inputs. Production outputs were measured in bushels and tons; input quantities were computed from input expenditures applying an input price index taken from the US Department of Agriculture in real US dollars. The dataset consisted of a 10-year (1998-2007) panel of 456 multi-output farms belonging to the Kansas Farm Management Association (KFMA). Data Envelopment Analysis (DEA) techniques were used to construct a non-parametric efficiency frontier and calculate technical efficiency (TE), allocative efficiency (AE), scale efficiency (SE), and overall or economic efficiency (OE) for each farm and each year. A discretionary input oriented DEA technique was used to assess the effect of capital availability as a farm input and its impact on farms' efficiencies. Efficiency scores in this problem were compared to the farms' scores when the level of debt was accounted for as a farm input. Panel data Tobit analysis was applied to the farms' inefficiency scores to investigate the causality of selected farm characteristics on technical, allocative, scale and overall inefficiencies. For the sampled farms and period, results confirmed that larger farms were more efficient than smaller ones. Farms specializing in livestock products, such as dairy and beef, were reported to be slightly more overall efficient than crop or mixed farms. Some economies of scope were found between custom work operations and crops. Financial structure of the farms was measured using the ratio of total debt to total assets for each farm. According to the results, larger leverage ratios increased all farm efficiencies. The positive effect of debt or capital availability in Kansas farms efficiencies was confirmed. The results of the technical efficiency discretionary DEA model agreed with this finding.

This Diploma thesis aims to theoretically define tools used by modern controlling and then practically apply them in specific company in Czech Republic. To analyse the chosen company will be used both strategic and operational controlling instruments. Calculations and analysis will help the company to understand the environment in which they operate and which is their current position. Finally I formulate opinions and recommendations about the currently used procedures in the company.