Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 64-67).

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Not only basic research in sciences, but also medicine, law, and manufacturing need statistical techniques, including graphics, to assess disagreement. For some items or individuals ⍳ = 1,2,---,ո suppose that pairs (X⍳,Y⍳) denote each item's measurements by two distinct methods or by two observers, or X⍳ and Y⍳ may be initial and repeat measurement scores, with discrepancy D⍳ = X⍳ - Y⍳. Disagreement may be characterized by location and scale parameters of discrepancy distributions. The present work primarily addresses estimation of central tendency - relative bias or median discrepancy (or discrepancy rate in some instances). Most previous literature on "agreement" or "reliability" instead concerns X, Y correlation, which can be regarded as the complement of discrepancy variance. (There is ambiguity or confusion about concepts of "reliability" in the literature of various applications.) Discrepancies D₁, D₂, • • •, Dո in practice often violate assumptions of standard statistical models and methods that have been commonly applied in studies of agreement. In particular, both X⍳ and Y⍳ generally incorporate measurement errors. Further, these two measurement error distributions for the ⍳th item need not be the same; and both distributions could depend on the magnitude µ⍳, of the item being measured. Hence, for example, discrepancy D⍳ could have variance proportional to the size of the item; and in general D₁, D₂, • • •, Dո are not identically distributed. Finally, the selection of items ⍳ = 1,2, • • •, ո often is not random. To estimate median discrepancy, we consider nonparametric confidence intervals corresponding to Student t test, sign test, Wilcoxon signed rank test, or other permutation tests. Several criteria are developed to compare the performance of one procedure relative to another, including expected ratio of confidence interval lengths (related to Pitman asymptotic relative efficiency of tests) and relative variability of interval lengths. Theoretical calculations and Monte Carlo simulation results suggest different procedural preferences for random sampling from different distributions. For discrepancies distributed non-identically, but symmetrically about a common median value, mixture sampling is used as an approximate model. This approach is related to a "random walk" (rather than random sample) model of D₁, D₂, • • •, Dո proposed particularly for discrepancies between counting processes. We also emphasize graphic methods, especially plots of difference of Y - X versus average (X + Y)/2, for exploratory analysis of discrepancy data and to choose appropriate statistical models and numerical methods. Various data sets are analyzed as examples of the methodology. / Science, Faculty of / Statistics, Department of / Graduate

Thesis (Ph. D.)--University of Chicago, Dept. of Economics, June 2000. / Includes bibliographical references. Also available on the Internet.

We investigate whether there are green returns to education, where formal education encourages pro-environmental behaviours using nationally representative surveys on environmental issues in Thailand. To establish the causal relationship between education and green behaviours, we exploit the instrumental variables strategy using the supply of state primary schooling i.e. the corresponding number of teachers per 1000 children, which varies over time and across regions as the instrument, while controlling for regional, cohort and income effects. We find that more years of schooling lead to a greater probability of taking knowledge-based environmentally-friendly actions a great deal, but not cost-saving pro-environmental actions. In addition, the paper finds no significant impact of formal education on concern about global warming nor the willingness to pay for environmental tax.

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Extensive testing is performed when a new aircraft is developed. Flight testing is costly and time consuming but there are aspects of the process that can be made more efficient. A program that estimates aerodynamic parameters during flight could be used as a tool when deciding to continue or abort a flight from a safety or data collecting perspective. The algorithm of such a program must function in real time, which for this application would mean a maximum delay of a couple of seconds, and it must handle telemetric data, which might have missing samples in the data stream. Here, a conceptual program for real-time estimation of aerodynamic parameters is developed. Two estimation methods and four methods for handling of missing data are compared. The comparisons are performed using both simulated data and real flight test data. The first estimation method uses the least squares algorithm in the frequency domain and is based on the chirp z-transform. The second estimation method is created by adding boundary terms in the frequency domain differentiation and instrumental variables to the first method. The added boundary terms result in better estimates at the beginning of the excitation and the instrumental variables result in a smaller bias when the noise levels are high. The second method is therefore chosen in the algorithm of the conceptual program as it is judged to have a better performance than the first. The sequential property of the transform ensures functionality in real-time and the program has a maximum delay of just above one second. The four compared methods for handling missing data are to discard the missing data, hold the previous value, use linear interpolation or regard the missing samples as variations in the sample time. The linear interpolation method performs best on analytical data and is compared to the variable sample time method using simulated data. The results of the comparison using simulated data varies depending on the other implementation choices but neither method is found to give unbiased results. In the conceptual program, the variable sample time method is chosen as it gives a lower variance and is preferable from an implementational point of view.

Instrumental variables have been used for a long time in the econometrics literature for the identification of the causal effect of one random variable, B, on another, C, in the presence of unobserved confounders. In the classical continuous linear model, the causal effect can be point identified by studying the regression of C on A and B on A, where A is the instrument. An instrument is an instance of a supplementary variable which is not of interest in itself but aids identification of causal effects. The method of instrumental variables is extended here to generalised linear models, for which only bounds on the causal effect can be computed. For the discrete instrumental variable model, bounds have been derived in the literature for the causal effect of B on C in terms of the joint distribution of (A,B,C). Using an approach based on convex polytopes, bounds are computed here in terms of the pairwise (A,B) and (A,C) distributions, in direct analogy to the classic use but without the linearity assumption. The bounding technique is also adapted to instrumental models with stronger and weaker assumptions. The computation produces constraints which can be used to invalidate the model. In the literature, constraints of this type are usually tested by checking whether the relative frequencies satisfy them. This is unsatisfactory from a statistical point of view as it ignores the sampling uncertainty of the data. Given the constraints for a model, a proper likelihood analysis is conducted to develop a significance test for the validity of the instrumental model and a bootstrap algorithm for computing confidence intervals for the causal effect. Applications are presented to illustrate the methods and the advantage of a rigorous statistical approach. The use of covariates and intermediate variables for improving the efficiency of causal estimators is also discussed.

Thesis advisor: Peter Gottschalk / This paper examines the impact of spatial variation in HIV rates on female marriage rates in Zambia. I formulate a search model that predicts lower marriage rates of educated females relative to uneducated females in regions with higher HIV rates. I use exogenous geographic variation in HIV rates to identify the causal effect of HIV on female marriage. The risk of HIV infection causes marriage rates to fall for educated females but rise for uneducated females. One explanation is that in high HIV regions: (1) educated females take the time to find a partner who will use condoms and get HIV tested, which delays marriage, and (2) uneducated females marry sooner because youth and virginity are prized by males, and employment opportunities are scarce. These findings imply that returns to education for young females are likely underestimated since they miss conceivably substantial health-related benefits. Is widow remarriage beneficial to child school enrollment? Women are widowed at relatively young ages in high-HIV areas of Sub Saharan Africa and are likely to have school-aged children. A main finding in the parental death literature is that the death of a mother hurts child education more so than does the death of a father. This masks important differences in child school enrollment across households who have experienced a father's death. This paper estimates the effect of widow remarriage on child school enrollment by exploiting regional variation in HIV, religion, and the sex ratio. The cross-country empirical results indicate that remarriage is detrimental to child enrollment for widows with less than six years of schooling, yet beneficial to child enrollment for widows with six or more years of schooling. This is consistent with (1) marital sorting by education (correlation=.7), (2) intra-household bargaining, and (3) differences in tastes for remarriage and schooling. A policy implication is that investing in female education in high-HIV areas - among those likely to become widows - can have multiplier effects, as there is complementarity between the returns to education on marriage market outcomes and children's education. / Thesis (PhD) — Boston College, 2011. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

This paper empirically assesses determinants of countries' fiscal rules suggested by the political science, sociology, and economics literature. We find several of these variables to be related to the stringency of fiscal rules, providing indirect evidence for the relevance of governments' deficit bias. These determinants may also serve as instruments in models with (endogenous) fiscal rules as explanatory variable.