Class of 2013 Abstract / Specific Aims: To assess the association between marginal tax rates (MTR) and innovative output of biotechnology firms. The MTR plays an important role in firms’ financing choices. Assessment of a firm’s tax status may reveal how firms decide on investment policies that affect R&D. Methods: A retrospective database analysis was used. Subjects included were firms within the biotechnology sector with the Standard Industrial Classification code of 2836 from 1980 - 2011. MTR Data was obtained from the S&P Compustat database, and Patent data was obtained from the U.S. Patent and Trademark Office. Changes in MTR’s on outcomes of patents were analyzed by performing an inferential analysis. Generalized estimating equations (GEE) were used, specifically utilizing a GEE regression with a negative binomial distributional family with log link, independent correlation structure and robust standard error variance calculation. Patents were regressed by the lagged change in MTR, after interest deductions. Main Results: The lag years 2 and 5 of the MTR change were statistically significant, (p = 0.031) and (p = 0.026) for each model respectively. Every one unit increase in the change of the MTRs was associated with large and significant drops in patents 78.8% (IRR = 0.212), 90.7% (IRR = 0.093), 92.7% (IRR = 0.073) at year 2 lag and 84.8% (IRR = 0.152), 92.6% (IRR = 0.074) at year 5 lag. Conclusion: An increase in the change of the MTR results in significant drops in patenting activity.

This thesis deals with software estimation, describes individual methods which may be used for constructing estimates in terms of effort, time and costs. It also deals with quality of estimate, the way it is used, the process of selection of the right method and other consequences. The main goal of thesis is creating a compact overview of currently known and used methods and approaches to constructing estimates. As a secondary target comes an example of practical use of estimate on a simulated project, where the way of choosing estimating method is presented, based on project attributes such as size, current stage of project or project type. For achieving these intentions, it is important to first set up basic framework of basic terms such as estimate, estimating, project and software project. Apart from just defining these terms, there are some necessary interrelations which must be explained before anything else. Another step is creating categorization of particular methods, their description, restrictions and suitable uses. The quality of an estimate, including factors which may bias, degrade or improve it, is another step towards final goal. Consequences, which may arise when wrong estimate has been provided, are one more piece of mosaic to this work. Lastly, case study demonstrate how to choose from estimating methods and use them on particular project. The expected benefits of the thesis are mainly in summarizing of existing approaches to the art of estimating broken down to categories, as well as list of principles and factors which affects an estimate and the ways estimates influence the project (in either positive or negative direction).

A large number of different methods in the field of estimation of IS/IT projects have been developed in past years. Those methods should be used with regard to several factors. The choice and the application of an estimation method should take into consideration the current phase of each project and the wide variety of external factors, whose composition and intensity are very diverse and individual. The accuracy of the estimation considerably influences the success of the whole project. Therefore it is crucial to pay special attention to the mentioned phenomenon. The aim of this diploma thesis is to provide a comprehensive overview of the issue of the estimation of large projects for fixed price. The objective is particularly fulfilled by the provided overview and definitions of the chosen methods, the list of the most common mistakes and the interpretation of the results of an analysis concerning the portfolio of projects that were realised in an international company. The expected outcome of the diploma thesis is represented by the definition of the procedurally correct and comprehensive description of the creation of the price for a project with the fixed extent of work, which can be guaranteed with a reasonable rate of risk by a supplier. Another outcome of the thesis is the interpreted outputs of the analysis concerning the portfolio of projects realised by the international supplier of IS/IT products and services, which can be exploited for more effective estimations and project portfolio management.

In this thesis, we propose to analyze panel count data using a spline-based sieve generalized estimating equation method with a semiparametric proportional mean model E(N(t)|Z) = Λ0(t) eβT0Z. The natural log of the baseline mean function, logΛ0(t), is approximated by a monotone cubic B-spline function. The estimates of regression parameters and spline coefficients are the roots of the spline based sieve generalized estimating equations (sieve GEE). The proposed method avoids assumingany parametric structure of the baseline mean function and the underlying counting process. Selection of an appropriate covariance matrix that represents the true correlation between the cumulative counts improves estimating efficiency. In addition to the parameters existing in the proportional mean function, the estimation that accounts for the over-dispersion and autocorrelation involves an extra nuisance parameter σ2, which could be estimated using a method of moment proposed by Zeger (1988). The parameters in the mean function are then estimated by solving the pseudo generalized estimating equation with σ2 replaced by its estimate, σ2n. We show that the estimate of (β0,Λ0) based on this two-stage approach is still consistent and could converge at the optimal convergence rate in the nonparametric/semiparametric regression setting. The asymptotic normality of the estimate of β0 is also established. We further propose a spline-based projection variance estimating method and show its consistency. Simulation studies are conducted to investigate finite sample performance of the sieve semiparametric GEE estimates, as well as different variance estimating methods with different sample sizes. The covariance matrix that accounts for the overdispersion generally increases estimating efficiency when overdispersion is present in the data. Finally, the proposed method with different covariance matrices is applied to a real data from a bladder tumor clinical trial.

No description available.

Estimates of agricultural production functions from experimental data for four different crops in relation to six variable inputs are calculated by this study. There are four basic sections in the study. The first section covers the review of production function concepts and the procedures and problems that specifically pertain to this study. Also the importance of joint economic-agronomic research efforts, methodologies and applications of agricultural production functions are cited. The second section includes the presentation data and postulated functional relationships in estimating production functions. Model building programs are used in developing three dimensional figures, which aid in the selection of the appropriate model. A multiple regression model using linear, non-linear and interaction terms is employed in deriving three production function for each crop. The problem of selecting a "best" model from the above three models is solved on the basis of economic theory, observed biologic physical production process, projected three dimensional production surfaces and statistical analyses. The polynomial form was selected as the "best" model for each crop. The third section of this study analyzes the results and the economic implications. Optimal rates of input use are determined. Qualification of these results are required because of the non significant statistical relationships including the F values of the regression coefficients and relatively low coefficient of determination (R2), and, also, because some optimal inputs values did not seem reasonable relative to observed rates. Further statistical analyses are carried out to determine the confidence interval for each input's marginal productivity and this results in unbounded solutions. As an alternative, the above confidence interval problem is rephrased as a system of equalities and solved simultaneously to obtain optimal input levels at the marginal productivities maximum and minimum values and these estimates are shown not to be confidence intervals. Finally, in the fourth section of this study, summary and conclusions are given. Also, limitation and recommendations to the study are discussed.

Indiana University-Purdue University Indianapolis (IUPUI) / The Electrical capacitance tomography (ECT) method has recently been adapted to obtain tomographic images of the cross section of a diesel particulate filter (DPF). However, a soot mass estimation algorithm is still needed to translate the ECT image pixel data to obtain soot load in the DPF. In this research, we propose an estimation method to quantify the soot load in a DPF through an inverse algorithm that uses the ECT images commonly generated by a back-projection algorithm. The grayscale pixel data generated from ECT is used in a matrix equation to estimate the permittivity distribution of the cross section of the DPF. Since these permittivity data has direct correlation with the soot mass present inside the DPF, a permittivity to soot mass distribution relationship is established first. A numerical estimation algorithm is then developed to compute the soot mass accounting for the mass distribution across the cross-section of the DPF as well as the dimension of the DPF along the exhaust flow direction. Firstly, ANSYS Electronic Desktop software is used to compute the capacitance matrix for different amounts of soot filled in the DPF, furthermore it also analyzed different soot distribution types applied to the DPF. The Analysis helped in constructing the sensitivity matrix which was used in the numerical estimation algorithm. Experimental data have been further used to verify the proposed soot estimation algorithm which compares the estimated values with the actual measured soot mass to validate the performance of the proposed algorithm.

No description available.

No description available.

Estimating is a critical aspect of every construction project. The most tedious component of an estimate is the quantity takeoff. Different tools are available to improve an estimator's productivity for a quantity takeoff. Faster quantity takeoffs enable estimator to bid more jobs. This study analyzed the time and accuracy of performing quantity takeoffs when using building information modeling (BIM) through a comparison study using Revit (BIM software) and On-Center's OST. In preparation for the study the researcher conducted a pilot study. Participants of the study were asked to perform a quantity takeoff using both Revit and OST. Their resulting data was then, analyzed comparing both the time and accuracy of each item taken off. The subjects' perspectives on the two different methods of performing takeoff are also included in the findings of this study. This study focused on a conceptual estimate takeoff for a small commercial building. The results of the study showed that, on average, those using BIM were able to perform the overall takeoff faster than those using OST while still maintaining equivalent accuracy. This study includes methods to improve productivity for quantity takeoff through an analysis of the takeoffs of counts, areas, and lengths. Findings of this study include methods, techniques, and cautions for performing a quantity takeoff using BIM.