Stabilized determination of geopotential coefficients by the mixed hom-BLUP approach

For the determination of geopotential coefficients, data can be used from rather different sources, e.g., satellite tracking, gravimetry, or altimetry. As each data type is particularly sensitive to certain wavelengths of the spherical harmonic coefficients it is of essential importance how they are treated in a combination solution. For example the longer wavelengths are well described by the coefficients of a model derived by satellite tracking, while other observation types such as gravity anomalies, delta g, and geoid heights, N, from altimetry contain only poor information for these long wavelengths. Therefore, the lower coefficients of the satellite model should be treated as being superior in the combination. In the combination a new method is presented which turns out to be highly suitable for this purpose due to its great flexibility combined with robustness

Policy Change: Concept, Measurement, and Causes. An Empirical Analysis of Climate Mitigation Policy

Policy change is one of the central issues of political science, public administration, sociology, and law studies. Research on this theme dates back to the late 1950s when scholars like Herbert Simon (1957), Charles Lindblom (1959), and Thomas Kuhn (1962) postulated “that general patterns of policy development cannot only be identified but predicted“ (Howlett and Cashore, 2009). Understanding and explaining policies and policy change became important with the increasing involvement of the state in more and more realms of social life: “The modern state is widely seen as an active and as a proactive state, increasingly managing, shaping, even creating its constituent population” (Pierson, 2004a). The last two decades have seen a tremendous activity in the explanation of policy change. Debates have centered on the role of ideas, actors, and institutions as competing and coordinated explanatory accounts. However, despite a plethora of studies, there is little generalization and comparability of findings. Recently, a number of scholars have attributed this inconsistency to the lack of a common understanding and operationalization of the concept of policy change – the so-called “dependent variable problem” (Cashore and Howlett, 2007). My dissertation attempts to make three major conceptual, methodological, and explanatory contributions towards solving this problem: 1) the thesis provides a theoretical framework for policy output and develops an empirical measurement for it; 2) it argues that one needs to consider entire policy portfolios rather than individual instruments for a meaningful assessment of policy change; 3) and it analyzes how the nature of the policy field affects the assessment and explanation of policy change

On the reliability of errors-in-variables models

Reliability has been quantified in a simple Gauss–Markov model (GMM) by Baarda (1968) for the application to geodetic networks as the potential to detect outliers – with a specified significance and power – by testing the least-squares residuals for their zero expectation property after an adjustment assuming “no outliers”. It was shown that, under homoscedastic conditions, the so-called “redundancy numbers” could very well serve as indicators for the “local reliability” of an (individual) observation. In contrast, the maximum effect of any undetectible outlier on the estimated parameters would indicate “global reliability”. This concept had been extended successfully to the case of correlated observations by Schaffrin (1997) quite a while ago. However, no attempt has been made so far to extend Baarda’s results to the (homoscedastic) errors-in-variables (EIV) model for which Golub and van Loan (1980) had found their – now famous – algorithm to generate the total least-squares (TLS) solution, together with all the residuals. More recently, this algorithm has been generalized by Schaffrin and Wieser (2008) to the case where a truly – not just elementwise –weighted TLS solution can be computed when the covariance matrix has the structure of a Kronecker–Zehfuss product. Here, an attempt will be made to define reliability measures within such an EIV-model, in analogy to Baarda’s original approach

The Impact of Missing Values on the Reliability Measures in a Linear Model

Reliability measures in linear models are used in geodetic science and elsewhere to quantify the potential to detect outliers and to suppress their impact on the regression estimates. Here we shall study the effect of missing values on these reliability measures with the idea that, under a proper design, they should not change drastically when such a situation occurs

Approximate Confidence Regions for Minimax-Linear Estimators

Minimax estimation is based on the idea, that the quadratic risk function for the estimate β is not minimized over the entire parameter space R^K, but only over an area B(β) that is restricted by a priori knowledge. If all restrictions define a convex area, this area can often be enclosed in an ellipsoid of the form B(β) = { β : β' Tβ ≤ r }. The ellipsoid has a larger volume than the cuboid. Hence, the transition to an ellipsoid as a priori information represents a weakening, but comes with an easier mathematical handling. Deriving the linear Minimax estimator we see that it is biased and non-operationable. Using an approximation of the non-central χ^2-distribution and prior information on the variance, we get an operationable solution which is compared with OLSE with respect to the size of the corresponding confidence intervals

Efficiency properties of weighted mixed regression estimation

This paper considers the estimation of the coefficient vector in a linear regression model subject to a set of stochastic linear restrictions binding the regression coefficients, and presents the method of weighted mixed regression estimation which permits to assign possibly unequal weights to the prior information in relation to the sample information. Efficiency properties of this estimation procedure are analyzed when disturbances are not necessarily normally distributed