<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.

This research assesses the performance of over-dispersed Poisson regression model and negative binomial model with count data. It examines the association between price plan features of mobile phone services and the number of people who adopt the plan. Mobile service data is used to estimate the model with a sample of one million customers running from February 2006 to September 2009. Under three main categories, customer type, age, and handset price, we run the model based on price plan features. Estimates are derived from the maximum likelihood estimation (MLE) method. Root mean squared error (RMSE) is used to observe the statistical fits of all the regression models. Then, we construct four estimation and holdout samples, leaving out one, three, six, and twelve months. The estimation constitutes the in-sample (IS) and the holdout represents the out-sample (OS). By estimating the IS, we predict the OS. Root mean squared error of prediction (RMSEP) is checked to see how accurate the prediction is. Results generally suggest that academic year start (AYS), seasonality, duration of months since launch of price plan (DMLP), basic fees, rate with no discount (RND), free call minutes (FCM), free data (FD), free text messaging (FTM), free perk rating (FPR), and handset support all show significant effect. The significance occurs depending on the segment. The RMSE and RMSEP show that the over-dispersed Poisson model outperforms the negative binomial model. Further implications and limitations of the results are discussed. / text

As one of the major data analysis methods, statistical models play an important role in traffic safety analysis. A common situation associated with crash data is the phenomenon known as overdispersion which has been discussed and investigated frequently in recent years. As such, researchers have proposed several models, such as the Poisson Gamma (PG) or Negative Binomial (NB), the Poisson-lognormal, or the Poisson-Weibull, to handle the overdispersion. Unfortunately, very few models have been proposed for specifically analyzing the sources of dispersions in the data. Better understanding of sources of variation and overdispersion could help in managing safety, such as establishing relationships and applying appropriate treatments or countermeasures, more efficiently. Given the limitations of existing models for exploring the source of overdispersion of crash data, this research examined a new model function that could be applied to explore sources of extra variability through the use of the Generalized Waring (GW) models. This model, which was recently introduced by statisticians, divides the observed variability into three components: randomness, internal differences between road segments or intersections, and the variances caused by other external factors that have not been included as covariates in the model. To evaluate these models, GW models were examined using both simulated and empirical crash datasets, and the results were compared to the most commonly used NB model and the recently developed NB-Lindley models. For model parameter estimation, both the maximum likelihood method and a Bayesian approach were adopted for better comparison. A simulation study was used to show the better performance of this model compared to NB model for overdispersed data, and then an application in the empirical crash data illustrates its capability of modeling data sets with great accuracy and exploring the source of overdispersion. The performances of hotspot identification for these two kinds of models (i.e., GW models and NB models) were also examined and compared based on the estimated models from the empirical dataset. Finally, bias properties related to the choice of prior distributions for parameters in GW model were examined by using a simulation study. In addition, the suggestions on the choice of minimum sample size and priors were presented for different kinds of datasets.

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of zero-inflated and hurdle regression models in the functions zeroinfl() and hurdle() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both model classes are able to incorporate over-dispersion and excess zeros - two problems that typically occur in count data sets in economics and the social and political sciences - better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inflated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences-better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (authors' abstract)

Metagenomics is the study of a collective microbial genetic content recovered directly from natural (e.g., soil, ocean, and freshwater) or host-associated (e.g., human gut, skin, and oral) environmental communities that contain microorganisms, i.e., microbiomes. The rapid technological developments in next generation sequencing (NGS) technologies, enabling to sequence tens or hundreds of millions of short DNA fragments (or reads) in a single run, facilitates the studies of multiple microorganisms lived in environmental communities. Metagenomics, a relatively new but fast growing field, allows us to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Also, it assists us to identify significantly different metabolic potentials in different environments. Particularly, metagenomic analysis on the basis of functional features (e.g., pathways, subsystems, functional roles) enables to contribute the genomic contents of microbes to human health and leads us to understand how the microbes affect human health by analyzing a metagenomic data corresponding to two or multiple populations with different clinical phenotypes (e.g., diseased and healthy, or different treatments). Currently, metagenomic analysis has substantial impact not only on genetic and environmental areas, but also on clinical applications. In our study, we focus on the development of computational and statistical methods for functional metagnomic analysis of sequencing data that is obtained from various environmental microbial samples/communities.

The aim of this study is to examine the effects of a market-based greenhouse gases price on green innovation by testing the Hicksian theory of induced innovation. To test whether causality exists, panel data compiled of 30 countries over 13 years (2005-2017) have been used. The study is restricted to the European Union emission trading scheme, where the price of EUA has been used as a market-based price for greenhouse gases. To capture the effect on innovation, an approximation for innovation in the form of patent counts have been employed using the patent category Y02 constructed by the EPO. The result suggests that green innovation is affected by the price of the EUA, total CO2 emissions and tax revenue from energy. This study employed a knowledge stock variable that was not found to be significant, contrary to previous literature on induced innovation. The incidence rate ratio associated with the  permits price indicates that a one euro increase in price would result in a 1.135 % increase in the patenting of green technology. The result suggests that a higher price in  permits would stimulate innovation of green technology within the European Union.

The main role of roadway lighting is to produce quick, accurate and comfortable visibility during nighttime conditions. It is commonly known that good lighting levels enable motorists, pedestrians and bicyclists to obtain necessary visual information in an effective and efficient manner. Many previous studies also proved that roadway lighting minimizes the likelihood of crashes by providing better visibility for roadway users. Appropriate and adequate roadway lighting illuminance levels for each roadway classification and pedestrian areas are essential to provide safe and comfortable usage. These levels are usually provided by national, or local standards and guidelines. The Florida Department of Transportation (FDOT) Plan Preparation Manual recommends a roadway lighting illuminance level average standard of 1.0 horizontal foot candle (fc) for all the roadway segments used in this research. The FDOT Plan Preparation Manual also states that this value should be considered standard, but should be increased if necessary to maintain an acceptable uniformity illuminance ratio. This study aimed to find the relationship between nighttime crash injury severity and roadway lighting illuminance. To accomplish this, the research team analyzed crash data and roadway lighting illuminance measured in roadway segments within the West Central Florida Region. An Ordered Probit Model was developed to understand the relationship between roadway lighting illuminance levels and crash injury severity. Additionally, a Negative Binomial Model was used to determine which roadway lighting illuminance levels can be more beneficial in reducing the counts of crashes resulting in injuries. A comprehensive literature review was conducted using longitudinal studies with and without roadway lighting. Results showed that on the same roadways there was a significant decrease in the number of nighttime crashes with the presence of roadway lighting. In this research, roadway lighting illuminance was measured every 40 feet using an Advanced Lighting Measurement System (ALMS) on a total of 245 centerline miles of roadway segments within the West Central Florida Region. The data were mapped and then analyzed using the existing mile post. During the process of crash data analysis, it was observed that rear-end collisions were the most common first harmful event observed in all crashes, regardless of the lighting conditions. Meanwhile, the average injury severity for all crashes, was found to be possible injury regardless of the lighting conditions (day, dark, dusk, and dawn). Finally, this research presented an Ordered Probit Model, developed to understand the existing relationship between roadway lighting illuminance levels and injury severity within the West Central Florida Region. It was observed that having a roadway lighting average moving illuminance range between 0.4 to 0.6 foot candles (fc) was more likely to have a positive effect in reducing the probability of injury severity during a nighttime crash. A Negative Binomial Model was conducted to determine if the roadway lighting average moving illuminance level, found on the Ordered Probit Model was beneficial in reducing crash injury severity during nighttime, would also be beneficial in reducing the counts of crashes resulting in injuries. It was observed that a roadway lighting average moving illuminance, range between 0.4 to 0.6 fc, was more likely to reduce the count of crashes resulting in injuries during nighttime conditions, thus increasing roadway safety. It was also observed that other factors such as pavement condition, site location (intersection or no intersection), number of lanes, and traffic volume can affect the severity and counts of nighttime crashes. The results of this study suggest that simply adding more roadway lighting does not make the roadway safer. The fact is that a reduction in the amount of roadway lighting illuminance can produce savings in energy consumption and help the environment by reducing light pollution. Moreover, these results show that designing roadway lighting systems go beyond the initial design process, it also requires continuous maintenance. Furthermore, regulations for new developments and the introduction of additional lighting sources near roadway facilities (that are not created with the intent of being used for roadway users) need to be created.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / Using a recent database on productivity of judges CearÃ, this study follows the proposal of Becker (1968) and proposes a model to investigate the determinants of crime in an empirical exercise for the municipalities of the state of CearÃ. The proposed models confront variables of economic development, agility and efficiency in judicial cases of illicit activity. The approach dealt with criminal activity according to the classification adopted in the Brazilian Penal Code, which is based on the legal injured: the person or property. Robust estimates confirm the beneficial effect of agility and efficiency of the judicial system in reducing criminal behavior, although evidencing a positive relationship between municipal development and torts. Together, this evidence suggests that public managers, besides of making strenuous efforts and resources on the intensification of surveillance and apprehension of criminals, as well as the improvement of social conditions, especially education, yet consider developing public policies that allow optimize investigation and punishment crimes. / Utilizando uma recente base de dados acerca da produtividade dos magistrados cearenses, este estudo segue a proposta de Becker (1968) e propÃe um modelo para investigar os determinantes da criminalidade em um exercÃcio empÃrico para os municÃpios do estado do CearÃ. Os modelos propostos confrontam variÃveis de desenvolvimento econÃmico, agilidade e eficiÃncia judiciÃria com casos de atividade ilÃcita. A abordagem tratou a atividade criminosa segundo a classificaÃÃo adotada no CÃdigo Penal Brasileiro, que se fundamenta no bem jurÃdico lesado: a pessoa ou o patrimÃnio. Estimativas robustas comprovam o efeito benÃfico da eficiÃncia e agilidade do sistema judiciÃrio na reduÃÃo do comportamento criminoso, muito embora evidenciem uma relaÃÃo positiva entre desenvolvimento municipal e atos ilÃcitos. Em conjunto, estas evidÃncias sugerem aos gestores pÃblicos que, alÃm de envidarem recursos e esforÃos na intensificaÃÃo da fiscalizaÃÃo e apreensÃo dos criminosos, bem como na melhoria das condiÃÃes sociais, sobretudo a educaÃÃo, considerem ainda desenvolver polÃticas pÃblicas que permitam otimizar a apuraÃÃo e a puniÃÃo dos crimes.