<p>Syftet med uppsatsen är att försöka förklara vad det är som påverkar tiden som en mamma ammar. För att undersöka vad det är som påverkar tiden som en mamma ammar, har en Zero inflated negative binomial-modell (ZINB-modell) tagits fram. Resultaten visar att det som avgör hur länge en mamma kommer att amma är: Graviditetens längd, mammans ålder, mammans rökvanor under graviditetens sista månader, mammans rökvanor samt mammans nationella ursprung.</p>

Syftet med uppsatsen är att försöka förklara vad det är som påverkar tiden som en mamma ammar. För att undersöka vad det är som påverkar tiden som en mamma ammar, har en Zero inflated negative binomial-modell (ZINB-modell) tagits fram. Resultaten visar att det som avgör hur länge en mamma kommer att amma är: Graviditetens längd, mammans ålder, mammans rökvanor under graviditetens sista månader, mammans rökvanor samt mammans nationella ursprung.

The gravity model for international trade is one of the most successful empirical models in trade literature. There is a long tradition to log-linearise the multiplicative model and to estimate the parameters of interest by least squares. But this practice is inappropriate for several reasons. First of all, bilateral trade flows are frequently zero and disregarding countries that do not trade with each other produces biased results. Second, log-linearisation in the presence of heteroscedasticity leads to inconsistent estimates in general. In recent years, the Poisson gravity model along with pseudo maximum likelihood estimation methods have become popular as a way of dealing with such econometric issues as arise when dealing with origin-destination flows. But the standard Poisson model specification is vulnerable to problems of overdispersion and excess zero flows. To overcome these problems, this paper presents zero-inflated extensions of the Poisson and negative binomial specifications as viable alternatives to both the log-linear and the standard Poisson specifications of the gravity model. The performance of the alternative model specifications is assessed on a real world example, where more than half of country-level trade flows are zero. (authors' abstract) / Series: Working Papers in Regional Science

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Made available in DSpace on 2016-06-02T20:06:07Z (GMT). No. of bitstreams: 1 4375.pdf: 903031 bytes, checksum: 03118f406867a5d7be3cbc63571d4a2b (MD5) Previous issue date: 2012-04-12 / Financiadora de Estudos e Projetos / The chemical induction of carcinogens in chemopreventive animal experiments is becoming increasingly frequent in biological research. The purpose of these biological experiments is to evaluate the effect of a particular treatment on the rate of tumors incidence in animals. In this work, the number of promoted tumors per animal will be parametrically modeled following the suggestions given by Kokoska (1987) and Freedman et al. (1993). The study of these chemopreventive experiments will be presented in the context of the destructive model proposed by Rodrigues et al. (2010) with terminal variable that allows or censures the experiment at time of the animal death. Since the data analyzed in this field are subject to excess of zeros (Freedman et al. (1993)), we propose for the number of promoted tumors a negative binomial distribution (NB), a zero-inflated Poisson distribution (ZIP), and a zero-inflated Negative Binomial distribution (ZINB). The selection of these models will be made through the likelihood ratio test and the AIC, BIC criteria. The estimation of its parameters will be obtained by using the method of maximum likelihood, and further simulation studies will also be realized. As a future proposition to finalize this project, it is suggested the Bayesian methodology as an alternative to the method of maximum likelihood via the EM algorithm. / A indução química de substâncias cancerígenas em experimentos quimiopreventivos em animais é cada vez mais frequente em pesquisas biológicas. O objetivo destes experimentos biológicos é avaliar o efeito de um determinado tratamento na taxa de incidência de tumores em animais. Neste trabalho o número de tumores promovidos por animal será modelado parametricamente seguindo as sugestões dadas por Kokoska (1987) e por Freedman et al. (1993). O estudo desses experimentos quimiopreventivos será apresentado no contexto do modelo destrutivo proposto por Rodrigues et al. (2010) com variável terminal que condiciona ou censura o experimento no instante de morte do animal. Os dados analisados possuem uma grande quantidade de zeros, portanto será proposto para o número de tumores promovidos as seguintes distribuições: binomial negativa, a distribuição de Poisson com zeros inflacionados e a distribuição binomial negativa com zeros inflacionados. A seleção destes modelos será feita através do teste da razão de verossimilhança e os critérios AIC, BIC. As estimativas dos respectivos parâmetros serão obtidas utilizando o método de máxima verossimilhança e serão feitos estudos de simulação. Para continuar este projeto, a proposta futura é utilizar a metodologia Bayesiana como alternativa ao método de máxima verossimilhança via algoritmo EM.