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Existing minefields left over from previous conflicts pose a grave threat to humanitarian relief operations, domestic everyday life, and future military operations. The remaining minefields in Afghanistan, from the decade long war with the Soviet Union, are just one example of this global problem. The purpose of this research is to develop a methodology that will predict areas where minefields are the most likely to exist through use of a GIS model. The concept is to combine geospatial data layers to produce a scored raster output of the most likely regions where minefields may exist. It is a "site suitability analysis" for minefield existence. The GIS model uses elevation and slope data, observer and defensive position locations, hydrographic features, transportation features, and trafficability estimates to form a minefield prediction surface. Through use of the NATO Reference Mobility Model (NRMMII) and the Digital Topographic Support System (DTSS), trafficability estimates are generated for specific vehicles under specific terrain and weather conditions in specific areas of interest. The model could be used to create prioritized maps for minefield detection sensors, demining teams, or for avoidance. These maps could define the "high payoff" search areas for remote sensors, such as ASTAMIDS, and positively identify minefields. These maps could also be used by humanitarian relief agencies for consideration when planning movement into areas that may contain minefields. The analysis includes a model calibration and sensitivity analysis procedure and compares the model output to known training minefield locations taken from two US Army training centers. The resultant Minefield Likelihood Surface has a 91% accuracy rate when compared to known training minefield data. / Master of Science

The Poisson distribution is commonly used in analyzing count data, and many insurance companies are interested in studying the related risk models and ruin probability theory. Over the past century, many different bivariate models have been developed in the literature. The bivariate Poisson distribution was first introduced by Campbell (1934) for modelling bivariate accident data. However, in some situations, a given dataset may possess over-dispersion compared to Poisson distribution which moti- vated researchers to develop alternative models to handle such situations. In this regard, Minkova and Balakrishnan (2014a) developed the Type I bivariate Polya- Aeppli distribution by using compounding with Geometric random variables and the trivariate reduction method. Inference for this Type I bivariate Polya-Aeppli distribution is the topic of this thesis. The parameters in a model are used to describe and summarize a given sample within a specific distribution. So, their estimation becomes important and the goal of estimation theory is to seek a method to find estimators for the parameters of interest that have some good properties. There exist many methods of finding estimators such as Method of Moments, Bayesian estimators, Least Squares, and Maximum Likelihood Estimators (MLEs). Each method of estimation has its own strength and weakness (Casella and Berger (2008)). Minkova and Balakrishnan (2014a) discussed the moment estimation of the parameters of the Type I bivariate Polya-Aeppli dis- tribution. In this thesis, we develop the likelihood inference for this model. A simulation study is carried out with various parameter settings. The obtained results show that the MLEs require more computational time compared to Moment estimation. However, Method of Moments (MoM) did not result in good estimates for all the simulation settings. In terms of mean squared error and bias, we observed that MLEs performed, in most of the settings, better than MoM. Finally, we apply the Type I bivariate Polya-Aeppli model to a real dataset containing the frequencies of railway accidents in two subsequent six year periods. We also carry out some hypothesis tests using the Wald test statistic. From these results, we conclude that the two variables belong to the same univariate Polya-Aeppli distribution but are correlated. / Thesis / Master of Science (MSc)

Nonprofit organizations are dependent on donations and volunteers to remain operational. Most rely on persuasive communications to inform, educate, and convince recipients of their messaging to respond in order to raise funds and generate volunteers. Though the marketing and psychology literature has examined charitable giving and volunteerism, the effectiveness of persuasive messages to affect philanthropy, gift-giving, and fundraising is a gap in the cause marketing literature (Dann et al. 2007). Because consumers rarely enter a situation without preexisting attitudes or beliefs, it is expected that individuals exposed to an advertisement by a nonprofit organization will look for ways to compare the messages within the ad to their own beliefs and attitudes. Two theories help explain the processing that takes place in relation to attitudes, beliefs, and persuasive communications – elaboration likelihood model (ELM) and the theory of planned behavior (TPB). The research presented here combines these theories to answer questions regarding behavioral intention related to donating and volunteering when individuals are exposed to certain persuasive messages from a nonprofit organization. Results show that one’s involvement with the advertisement combines with one’s attitude toward donating to help determine propensity to donate and the amount of the donation. However, this is dependent upon the message in the ad. When messages indicate that others are supportive of the cause, donations increase when one is more involved with the ad and is generally agreeable to donating. But these messages have the opposite effect when one is not involved with the ad – donations decrease when the message indicates others support the cause. And when messages indicate that even a minimal donation is possible, the attitude driver has no effect on donation behavior. However, when involvement is low, one’s age plays a role in driving individuals toward action, with older people more driven to give when exposed to supportive messages under low involvement conditions than younger groups. For individuals who tend to rely on referents for their own actions, differing messages in advertisements have little effect whether they are involved with the ad or not. That is, in most cases, only their involvement with the ad seems to be the real indicator of behavior. That said, the message that indicates that minimum giving is acceptable seems to affect donations, as individuals more prone to seek referent input rely on this message to help direct behavior, but not volunteerism. But, the cues were more readily adopted by those who were not highly involved. This research contributes to the field of cause marketing in several ways. First, it exposed involvement with the advertisement as the primary driver for behavioral intention in a nonprofit context over one’s preexisting attitudes and beliefs. Second, it identified varying response patterns that individuals have to specific advertising messages based on their level of involvement and strength of those beliefs and attitudes. Third, it augmented the integrated ELM-TPB theoretical model by demonstrating that attitude toward the ad can play a role in consumer decision making. Fourth, it identified age as a factor in behavioral intention related to nonprofit organizations in two specific instances: 1) when attitude and involvement combine for older individuals exposed to normative messages, and 2) when subjective norms and involvement combine for younger individuals exposed to messages that legitimize minimal effort. And fifth, it uncovered implications for managers to develop strategic messages that can increase target audience involvement and positively affect donations and volunteerism.

Ett budskap uppfattas olika beroende på avsändare. Elaboration Likelihood Model (ELM) beskriver hur människor kan bearbeta ett budskap på olika sätt, centralt eller perifert, beroende på motivationsnivå. Framing sker när människor tillskriver egna bedömningar av olika ord, vilket kan påverka deras beslutfattande och beteende. Med en experimentell design undersöktes hur ett budskap relaterat till Covid-19 med en forskare eller en influencer som förment avsändare kan uppfattas av individer med olika grad av motivation. 136 studenter (80 kvinnor och 56 män) besvarade en webbbaserad enkät. En tvåvägs variansanalys visade, i strid med ELM, att deltagarna instämde i högre utsträckning i ett budskap när avsändaren var en forskare än när avsändaren var en influencer oavsett motivationsnivå. Högmotiverade deltagare uppfattade däremot budskapets språkliga kvalitet som högre än lågmotiverade. Högmotiverade deltagare gjort en skillnad mellan forskare och influencer. Framing kan vara en förklaring till varför deltagarna instämmer mer eller mindre med forskaren och influencern.

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