We discuss the problem of optimal allocation of the design points of an experiment for the case where the trials may fail with non-zero probability. Numerical results for D-optimal designs are given for estimating the coefficients of a polynomial regression. For small sample sizes these designs may deviate substantially from the corresponding designs in the case of certain response. They can be less efficient, but are less affected by failing trials. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

This report analyzes the impact of some important factors on the prediction of detection probabilities for radiation portal monitors (RPMs). The application of innovative detection technology to improve operational sensitivity of RPMs has received increasing attention in recent decades. In particular, two alarm algorithms, gross count and energy windowing, have been developed to try to distinguish between special nuclear material (SNM) and naturally occurring radioactive material (NORM). However, the use of the two detection strategies is quite limited due to a very large number of unpredictable threat scenarios. We address this problem by implementing a new Monte Carlo radiation transport simulation approach to model a large set of threat scenarios with predefined conditions. In this report, our attention is focused on the effect of two important factors on the detected energy spectra in RPMs, the mass of individual nuclear isotopes and the thickness of shielding materials. To study the relationship between these factors and the resulting spectra, we apply several advanced statistical regression models for different types of data, including a multinomial logit model, an ordinal logit model, and a curvilinear regression model. By utilizing our new simulation technique together with these sophisticated regression models, we achieve a better understanding of the system response under various conditions. We find that the different masses of the isotopes change the isotopes’ effect on the energy spectra. In analyzing the joint impact of isotopes’ mass and shielding thickness, we obtain a nonlinear relation between the two factors and the gross count of gamma photons in the energy spectrum. / text

This study focused on measuring courteous behavior on the University of Texas at Austin (UT) students on campus. This behavior was measured through analyzing various factors involved when a person opened the door for another. The goal was to determine which factors would significantly affect the probability that a person would hold a door for another. Three UT buildings with no automatic doors were selected (RLM, FAC and GRE), and 200 pairs of students at each location were observed to see whether they would open doors for others. These subjects were not disturbed during the data collection process. For each observation, the door holding conditions, genders, position (whether it was the one who opened the door or the recipient of this courteous gesture, abbreviated as recipient), distance between the person opening the door and the recipient, and the number of recipients were recorded. Descriptive statistics and logistic regression were used to analyze the data. The results showed that the probability of people opening the doors for others was significantly affected by gender, position, distance between the person opening the door and the recipient, the number of recipients, and the interaction term between gender and position. The study revealed that men had a slightly higher propensity of opening the doors for the recipients. The odds for men were a multiplicative factor of 1.09 of that for women on average, holding all other factors constant. However, women had much higher probability of having doors held open for them. The odds for men were a multiplicative factor of 0.55 of that for women on average, holding all other factors constant. In terms of the distance between the person opening the door and the recipient, for each meter increase in distance, the odds that the door would be held open would decrease by a multiplicative factor of 0.40 on average. Additionally, for each increase in number of recipients, the odds that the door would be held open would increase by a multiplicative factor of 1.32 on average. / text

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Statistics / Master / Master of Philosophy

På bara några decennier har kravet på utbildning vuxit markant och därmed också antalet universitetsstuderande. Syftet med denna uppsats har varit att undersöka huruvida det lönar sig att vidareutbilda sig på universitetsnivå och i vilken utsträckning det finns skillnader för män respektive kvinnor inom en mans och en kvinnodominerad utbildning. Fyra regressioner har genomförts. Därefter har en jämförelse skett mellan de olika livsinkomsternas nuvärden. Livsinkomsten har räknats fram genom att subtrahera nuvärdet av alternativkostnaderna från nuvärdet av livsinkomsterna. Vi har funnit att man gör en avkastning på att utbilda sig samt att det finns påtagliga skillnader mellan män och kvinnor både inom den mans- och den kvinnodominerade utbildningen.

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Theodor Adorno’s theory of listener regression describes the process by which music industry forces transform listeners from independent social creatures into passive, compliant consumers of musical product. Listener regression is the basis of the broader theory of culture industry consumer regression which Adorno later developed, by which the culture industry creates and reinforces the passive compliance of consumers of all cultural products. By further extension, it is my theory that listener regression is the key to understanding how the phenomenon of culture industry dominance over its consumers applies to the whole range of non-cultural as well as cultural consumer products. In the second chapter of this paper I discuss how listeners of music industry output are made to regress: through the standardization of musical form, the repetition which flows from standardization and the attention to musical detail which has no formal function. I consider those childhood traits which constitute listener regression. Freud’s theory of regression is discussed and distinguished. It is important to filter out the negativity which infuses much of Adorno’s writing on listener regression. That negativity arises arguably either from Adorno’s own acute musical perception which exceeds the average listener’s or from the era of growing totalitarianism in which he formed his views. That negativity unnecessarily clouds the significance of the relationship between the music industry and average listeners, between the larger culture industry and its consumers, and between the exponentially larger consumer industry and all consumers generally. Country music as the epitome of the kind of “popular music” which Adorno argues results in the regression of its listeners is the topic of the third chapter. I conclude that country music does meet Adorno’s criteria for listener regressive music. I also conclude that within the confines Adorno finds so soul-depriving is amazingly creative music and musicality which responds to some of humankind’s most basic needs. In the last chapter, I discuss music as a fundamental component of human society which responds to some deep social need. By showing us how one segment of the consumer industry has turned that need to its commercial benefit, Adorno’s theory can help show parallels with those profit-motivated forces within other segments of our consumer society, for instance with the manipulation of the basic need for sustenance by the fast-food industry. The paper concludes with a number of observations about the manipulative nature of the powerful commercial pressure put on us as consumers, and offers a suggestion or two for dealing with that pressure.

Several methods are available for the treatment of missing data. Most of the methods are based on the assumption that data are missing completely at random (MCAR). However, data sets that are MCAR are rare in psycho-educational research. This gives rise to the need for investigating the performance of missing data treatments (MDTs) with non-randomly or systematically missing data, an area that has not received much attention by researchers in the past. In the current simulation study, the performance of four MDTs, namely, mean substitution (MS), pairwise deletion (PW), expectation-maximization method (EM), and regression imputation (RS), was investigated in a linear multiple regression context. Four investigations were conducted involving four predictors under low and high multiple R² , and nine predictors under low and high multiple R² . In addition, each investigation was conducted under three different sample size conditions (94, 153, and 265). The design factors were missing pattern (2 levels), percent missing (3 levels) and non-normality (4 levels). This design gave rise to 72 treatment conditions. The sampling was replicated one thousand times in each condition. MDTs were evaluated based on accuracy of parameter estimates. In addition, the bias in parameter estimates, and coverage probability of regression coefficients, were computed. The effect of missing pattern, percent missing, and non-normality on absolute error for R² estimate was of practical significance. In the estimation of R², EM was the most accurate under the low R² condition, and PW was the most accurate under the high R² condition. No MDT was consistently least biased under low R² condition. However, with nine predictors under the high R² condition, PW was generally the least biased, with a tendency to overestimate population R². The mean absolute error (MAE) tended to increase with increasing non-normality and increasing percent missing. Also, the MAE in R² estimate tended to be smaller under monotonic pattern than under non-monotonic pattern. MDTs were most differentiated at the highest level of percent missing (20%), and under non-monotonic missing pattern. In the estimation of regression coefficients, RS generally outperformed the other MDTs with respect to accuracy of regression coefficients as measured by MAE . However, EM was competitive under the four predictors, low R² condition. MDTs were most differentiated only in the estimation of β₁, the coefficient of the variable with no missing values. MDTs were undifferentiated in their performance in the estimation for b₂,...,bp, p = 4 or 9, although the MAE remained fairly the same across all the regression coefficients. The MAE increased with increasing non-normality and percent missing, but decreased with increasing sample size. The MAE was generally greater under non-monotonic pattern than under monotonic pattern. With four predictors, the least bias was under RS regardless of the magnitude of population R². Under nine predictors, the least bias was under PW regardless of population R². The results for coverage probabilities were generally similar to those under estimation of regression coefficients, with coverage probabilities closest to nominal alpha under RS. As expected, coverage probabilities decreased with increasing non-normality for each MDT, with values being closest to nominal value for normal data. MDTs were most differentiated with respect to coverage probabilities under non-monotonic pattern than under monotonic pattern. Important implications of the results to researchers are numerous. First, the choice of MDT was found to depend on the magnitude of population R², number of predictors, as well as on the parameter estimate of interest. With the estimation of R² as the goal of analysis, use of EM is recommended if the anticipated R² is low (about .2). However, if the anticipated R² is high (about .6), use of PW is recommended. With the estimation of regression coefficients as the goal of analysis, the choice of MDT was found to be most crucial for the variable with no missing data. The RS method is most recommended with respect to estimation accuracy of regression coefficients, although greater bias was recorded under RS than under PW or MS when the number of predictors was large (i.e., nine predictors). Second, the choice of MDT seems to be of little concern if the proportion of missing data is 10 percent, and also if the missing pattern is monotonic rather than non-monotonic. Third, the proportion of missing data seems to have less impact on the accuracy of parameter estimates under monotonic missing pattern than under non-monotonic missing pattern. Fourth, it is recommended for researchers that in the control of Type I error rates under low R² condition, the EM method should be used as it produced coverage probability of regression coefficients closest to nominal value at .05 level. However, in the control of Type I error rates under high R² condition, the RS method is recommended. Considering that simulated data were used in the present study, it is suggested that future research should attempt to validate the findings of the present study using real field data. Also, a future investigator could modify the number of predictors as well as the confidence interval in the calculation of coverage probabilities to extend generalization of results.

Transportation accidents and the resulting fires are an important field of study. At the University of Waterloo Live Fire Research Facility (UWLFRF), an experiment was conducted in partnership with Sandia National Laboratories in Albuquerque, New Mexico. This experiment was designed to simulate an aircraft accident where fuel is spilled on the runway and is subsequently ignited. A crosswind pushes the 2.0 m diameter pool fire towards the aircraft fuselage and the conditions around the fire are monitored. Literature on the subject is examined first, examining the relationship between the fire, the crosswind, and the 2.7 m diameter blocking object (aircraft fuselage). A full wind characterization is then presented of the UWLFRF both with and without the blocking object in place, using five distinct wind speeds ranging from 3 m/s to 13.5 m/s. Turbulence intensity measurements are made on the centerline of the facility when possible. Details about the two sets of live fire tests are presented, a control experiment without the blocking object in place and then fire tests with the blocking object in place. Additionally, the control experiment has two different setups, one involving a floor surround in order to diminish the effect of the forward facing step on the front of the fuel pan. The fuel regression rate, the wind speed, the ambient conditions and the heat flux near the fuel pan are monitored during each live fire test. The fuel regression rate, defined as the rate at which the height of the liquid fuel level decreases as the fire burns, is then analyzed versus all other monitored variables. During no blocking object tests, trends of increasing wind speed and increasing heat flux on some gauges and decreasing flux on others was observed with increasing fuel regression rate when the floor surround was in place. During no blocking object tests without the floor surround and tests with the blocking object in place, no strong trends were observed when comparing the monitored variables. The ambient conditions were not observed to have an effect on any test. The average fuel regression for tests without the blocking object in place is 4.0 mm/min without the floor surround, and 4.4 mm/min with it in place. With the blocking object in place the average fuel regression rate was measured to be 4.8 mm/min using load cells and 4.1 mm/min using the sight glass.