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In the year of 2013, Swedish media accused the Swedish police for the implementation of discriminatory internal controls of foreigners, in Malmö, Stockholm and other places across Sweden. The controls were soon to be linked to project REVA. In this thesis, I examine how the internal controls of foreigners measure up to the Swedish Aliens act and whether or not the accusations of discriminatory controls against individuals with a perceived foreign appearance, are correct. The examination includes a legal approach, where I use practical legal method to analyze the provisions of the Aliens act, as well as an evolving conceptual approach, where I evolve the concept of discrimination in order to apply it to the controls. I found that it is possible to link project REVA to the external work with the internal controls of foreigners, despite the denial from people involved. I also found that, because of unclear regulations regarding the implementation of the internal controls of foreigners, it is not possible to assess how the controls measure up to the Swedish Aliens act. Some of the controls, most probably, can be identified as directly and indirectly discriminatory. The problem with this statement is the difficulty to measure police officers decision-making in order to confirm the discriminatory behavior. However, I argue that it is possible to claim that the Aliens act might be indirectly discriminatory. The results of this thesis indicate that the Swedish police should review their practices regarding internal controls of foreigners.

The purpose of this experimental study is to examine the effects of recasts on narrowing overgeneralized grammar in the second language (L2). The study involved testing three major hypotheses of the mechanisms underlying recasts: the direct contrast hypothesis (Saxton, 1997, 2000), the additional input hypothesis (Gass, 1997; Gass & Mackey, 2007; Long, 1996, 2007), and the enhanced salience hypothesis (Leeman, 2003). Two structures (adjective ordering and indirect passives) were selected for this study, mainly because it was assumed that Japanese learners of English might produce overgeneralized rules that allow incorrect structures because of their first language (L1) influence. The participants were 97 Japanese university students learning English as a foreign language in Japan. They were randomly assigned to four treatment groups: recast (the REC Group), non-contingent positive evidence (the POS Group), recast plus additional input (the REC+ Group), and input with enhanced salience (the SAL Group). A pretest, posttest, and delayed-posttest design were employed. The measurement instruments were an oral production task, elicited imitation task, and untimed grammaticality judgment task, each of which was designed to elicit participants’ implicit and explicit knowledge about adjective ordering and indirect passives. Thus, the independent variable was the treatment conditions, and the dependent variable were the test scores regarding ungrammaticality of the overgeneralized rules of the target structures. After data screening, the data from 75 of the 97 participants were analyzed for adjective ordering, and the data from 90 participants were analyzed for indirect passives. The results showed that the POS Group did not improve on all the measures for adjective ordering and indirect passives; thus, it was suggested that positive evidence was not sufficient for the participants to narrow overgeneralized rules for the target structures. The findings indicated that for adjective ordering, medium effect sizes for the comparison of the POS and REC Groups were obtained on the grammaticality judgment tests for the pretest-posttest and pretest-delayed posttest comparisons. Thus, based on these effect sizes, the direct contrast hypothesis was partially supported (i.e., for one of the two structures and one measure of the three tests). Furthermore, on the basis of the results that the REC and REC+ Groups did not differ significantly on any measure and that on the adjective-ordering grammaticality judgment tests, the comparison between the REC+ Group and the POS Group obtained a medium effect size for the pretest-delayed posttest comparison, the provision of recasts in the REC and REC+ Groups was effective at least for the adjective-ordering grammaticality judgment tests; however, additional input alone did not have an impact on L2 learners’ retreat from the overgeneralized rules. Finally, the results showed that the POS and SAL Groups did not differ significantly and that there existed a difference in the performance on the adjective-ordering grammaticality judgment tests between the REC Group and the SAL Groups. Salience might not be effective in helping L2 learners retreat from overgeneralized rules, and the enhanced salience hypothesis can be interpreted as being limited to learning some linguistic structures. In conclusion, the present study provides empirical evidence that L2 learners can persist in using incorrect overgeneralized rules due to L1 rules and that ten tokens of the target structures are not effective for L2 learners to retreat from the overgeneralized rules. Furthermore, the findings lend support to the direct contrast hypothesis as a mechanism underlying recasts for one of the two target structures. / Teaching & Learning

The g - and - h distributional family is generated from a relatively simple transformation of the standard normal. By changing the skewness and elongation parameters g and h, this distributional family can approximate a broad spectrum of commonly used distributional shapes, such as normal, lognormal, Weibull and exponential. Consequently, it is easy to use in simulation studies and has been applied in multiple areas, including risk management, stock return analysis and missing data imputation studies. The current available methods to estimate the g - and - h distributional family include: letter value based method (LV), numerical maximal likelihood method (NMLE), and moment methods. Although these methods work well when no outliers or contaminations exist, they are not resistant to a moderate amount of contaminated observations or outliers. Meanwhile, NMLE is a computational time consuming method when data sample size is large. In this dissertation a quantile based least squares (QLS) estimation method is proposed to fit the g - and - h distributional family parameters and then derive its basic properties. Then QLS method is extended to a robust version (rQLS). Simulation studies are performed to compare the performance of QLS and rQLS methods with LV and NMLE methods to estimate the g - and - h parameters from random samples with or without outliers. In random samples without outliers, QLS and rQLS estimates are comparable to LV and NMLE in terms of bias and standard error. On the other hand, rQLS performs better than other non-robust method to estimate the g - and - h parameters when moderate amount of contaminated observations or outliers exist. The flexibility of the g - and - h distribution and the robustness of rQLS method make it a useful tool in various fields. The boxplot (BP) method had been used in multiple outlier detections by controlling the some-outside rate, which is the probability of one or more observations, in an outlier-free sample, falling into the outlier region. The BP method is distribution dependent. Usually the random sample is assumed normally distributed; however, this assumption may not be valid in many applications. The robustly estimated g - and - h distribution provides an alternative approach without distributional assumptions. Simulation studies indicate that the BP method based on robustly estimated g - and - h distribution identified reasonable number of true outliers while controlling number of false outliers and some-outside rate compared to normal distributional assumption when it is not valid. Another application of the robust g - and - h distribution is as an empirical null distribution in false discovery rate method (denoted as BH method thereafter). The performance of BH method depends on the accuracy of the null distribution. It has been found that theoretical null distributions were often not valid when simultaneously performing many thousands, even millions, of hypothesis tests. Therefore, an empirical null distribution approach is introduced that uses estimated distribution from the data. This is recommended as a substitute to the currently used empirical null methods of fitting a normal distribution or another member of the exponential family. Similar to BP outlier detection method, the robustly estimated g - and - h distribution can be used as empirical null distribution without any distributional assumptions. Several real data examples of microarray are used as illustrations. The QLS and rQLS methods are useful tools to estimate g - and - h parameters, especially rQLS because it noticeably reduces the effect of outliers on the estimates. The robustly estimated g - and - h distributions have multiple applications where distributional assumptions are required, such as boxplot outlier detection or BH methods. / Statistics

The dissertation examines advancements in the methods and techniques used in the field of econometrics. These advancements include: (i) a re-examination of the underlying statistical foundations of statistical models with binary dependent variables. (ii) using feed-forward backpropagation artificial neural networks for modeling dichotomous choice processes, and (iii) the estimation of unconditional demand elasticities using the flexible multistage demand system with asymmetric partitions and fixed effects across time. The first paper re-examines the underlying statistical foundations of statistical models with binary dependent variables using the probabilistic reduction approach. This re-examination leads to the development of the Bernoulli Regression Model, a family of statistical models arising from conditional Bernoulli distributions. The paper provides guidelines for specifying and estimating a Bernoulli Regression Model, as well as, methods for generating and simulating conditional binary choice processes. Finally, the Multinomial Regression Model is presented as a direct extension. The second paper empirically compares the out-of-sample predictive capabilities of artificial neural networks to binary logit and probit models. To facilitate this comparison, the statistical foundations of dichotomous choice models and feed-forward backpropagation artificial neural networks (FFBANNs) are re-evaluated. Using contingent valuation survey data, the paper shows that FFBANNs provide an alternative to the binary logit and probit models with linear index functions. Direct comparisons between the models showed that the FFBANNs performed marginally better than the logit and probit models for a number of within-sample and out-of-sample performance measures, but in the majority of cases these differences were not statistically significant. In addition, guidelines for modeling contingent valuation survey data and techniques for estimating median WTP measures using FFBANNs are examined. The third paper estimates a set of unconditional price and expenditure elasticities for 49 different processed food categories using scanner data and the flexible and symmetric translog (FAST) multistage demand system. Due to the use of panel data and the presence of heterogeneity across time, temporal fixed effects were incorporated into the model. Overall, estimated price elasticities are larger, in absolute terms, than previous estimates. The use of disaggregated product groupings, scanner data, and the estimation of unconditional elasticities likely accounts for these differences. / Ph. D.

The major Hot-Mix Asphalt (HMA) input for mechanistic-empirical (M-E) flexible pavement design is the dynamic complex modulus obtained from either the uniaxial or triaxial compressive dynamic modulus test. Furthermore, as part of the performance-based mix design process, the triaxial dynamic modulus has been selected to predict rutting and fatigue cracking, and the Indirect Tension (IDT) creep compliance test to predict low-temperature thermal cracking. The creep compliance and dynamic modulus are measured responses (viscoelastic functions) of viscoelastic materials under transient and cyclic loading, respectively. Under the assumptions of linearity, linear viscoelastic functions are equivalent. Moreover, these properties should be the same whether they are obtained from a uniaxial compressive or IDT test. For this dissertation, we tested the applicability of linear viscoelastic (LVE) theory to HMA mixes and determined whether HMA need to be modeled as a bimodular material to analyze IDT creep compliance test results. The need to model HMA as a bimodular material is a result of a number of studies that suggest that HMA tensile and compressive properties are different. A testing program was developed to experimentally measure the uniaxial compression, and IDT creep compliance, and the uniaxial compression dynamic modulus for different HMA mixes. The uniaxial compressive creep compliance and dynamic modulus master curves are constructed and the shift factors obtained from each test are compared. Interconversion between the creep compliance and dynamic modulus experimental results confirm the applicability of LVE theory for the HMA mixes investigated. Based on the applicability of LVE theory, a methodology to determine HMA LVE properties from the combined creep compliance and dynamic modulus test results was developed. As a practical application that is relevant to the M-E flexible pavement design procedure, LVE theory was used and compared to proposed approximate methods to perform the conversion of testing frequency to loading time. Specifically, dynamic modulus results were converted to relaxation modulus, creep compliance, and resilient modulus. Finally, the HMA IDT creep compliance test results at low and intermediate temperature (<20oC) were successfully analyzed using a HMA bimodular material model based on the Ambartsumyan model. The difference between the compressive modulus and the modulus calculated from the IDT test using Hondros' stress distribution is calculated. In addition, a method to determine the compressive-to-tensile modulus ratio using uniaxial compressive and IDT test results is illustrated for one of the tested HMA mixes. / Ph. D.