"Scientists usually need to understand the extent of the association of two attributes, and the data are typically presented in two-way categorical tables. In science, the chi-squared test is routinely used to analyze data from such tables. However, in many applications the chi-squared test can be defective. For example, when the sample size is small, the chi-squared test may not be applicable. The terms small area" and local area" are commonly used to denote a small geographical area, such as a county. If a survey has been carried out, the sample size within any particular small area may be too small to generate accurate estimates from the data, and a chi-squared test may be invalid (i.e., expected frequencies in some cells of the table are less than ?ve). To deal with this problem we use Bayesian small area estimation. Because it is used toorrow strength" from related or similar areas. It enhances the information of each area with common exchangeable information. We use a Bayesian model to estimate a Bayes factor to test the independence of the two variables. We apply the model to test for the independence between bone mineral density (BMD) and body mass index (BMI) from 31 counties and we compare the results with a direct Bayes factor test. We have also obtained numerical and sampling errors; both the numerical and sampling errors of our Bayes factor are small. Our model is shown to be much less sensitive to the speci?cation of the prior distribution than the direct Bayes factor test which is based on each area only."

This study investigates perceptual effects of discharge rate adaptation in the auditory-nerve fibers. Discrimination tests showed that brief synthetic stimuli with stationary formants and periodic source were better discriminated when they had an abrupt as opposed to a gradual onset (non-adapted vs adapted condition). This effect was not observed for corresponding stimuli with noise source. Discrimination among synthetic /da/ stimuli (abrupt onsets) was worse than among /ad/ stimuli when the respective onset and offset frequencies of the second formant (F2) were varied. Similar results were obtained for /ba/ and /ab/. The low discrimination rate in consonant-vowel stimuli (CV) was explained in terms of sensory smearing of spectral information due to rapid formant transitions. Discrimination improved when the smearing effect was reduced by holding the onset formant pattern over a certain period of time of about 1 6ms. The relatively high discrimination score for the VC stimuli was explained by residual masking; extending the VC offset did not improve discrimination. Discrimination of place of articulation in CV syllables was examined in the light of sensory smearing. Two continua of /bu-du/ and /ba-da/ utterances were used in discrimination and identification experiments. It was observed that the discrimination peak for /Cu/ was displaced from the /b/-/d/ boundary, towards a flat F2 transition, suggesting that optimal place discrimination is related to the stability of the auditory representations generated at onset. This result is discussed in relation to current views of categorical perception. / För att köpa boken skicka en beställning till exp@ling.su.se/ To order the book send an e-mail to exp@ling.su.se

Concept drift detection, the identfication of changes in data distributions in streams, is critical to understanding the mechanics of data generating processes and ensuring that data models remain representative through time [2]. Many change detection methods utilize statistical techniques that take numerical data as input. However, many applications produce data streams containing categorical attributes. In this context, numerical statistical methods are unavailable, and different approaches are required. Common solutions use error monitoring, assuming that fluctuations in the error measures of a learning system correspond to concept drift [4]. There has been very little research, though, on context-based concept drift detection in categorical streams. This approach observes changes in the actual data distribution and is less popular due to the challenges associated with categorical data analysis. However, context-based change detection is arguably more informative as it is data-driven, and more widely applicable in that it can function in an unsupervised setting [4]. This study offers a contribution to this gap in the research by proposing a novel context-based change detection and adaptation algorithm for categorical data, namely Fine-Grained Change Detection in Categorical Data Streams (FG-CDCStream). This unsupervised method exploits elements of ensemble learning, a technique whereby decisions are made according to the majority vote of a set of models representing different random subspaces of the data [5]. These ideas are applied to a set of concept drift detector objects and merged with concepts from a recent, state-of-the-art, context-based change detection algorithm, the so-called Change Detection in Categorical Data Streams (CDCStream) [4]. FG-CDCStream is proposed as an extension of the batch-based CDCStream, providing instance-by-instance analysis and improving its change detection capabilities especially in data streams containing abrupt changes or a combination of abrupt and gradual changes. FG-CDCStream also enhances the adaptation strategy of CDCStream producing more representative post-change models.

<p> In this thesis we study extensive subcategories of various categories of Hausdorff spaces and continuous maps, and of Hausdorff uniform spaces and uniformly continuous maps, In particular, we obtain new methods to construct extensive subcategories which can be applied to many categories and give us an inclusive relationship between reflective subcategories of Haus and coreflective subcategories of Top. We consider perfect onto projectivity in those categories. The relationships between n-compact spaces and topologically complete spaces are discussed. </p> / Thesis / Doctor of Philosophy (PhD)

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Intensive selection for increased milk production over many generations has led to growing genetic similarity and increased relationships in dairy population. In the current study, inbreeding depression was estimated for number of days to first service, summit milk, conception by 70 days non-return, and calving rate with a linear mixed model (LMM) approach and for calving difficulty, calf mortality with a Bayesian threshold model (BTM) for categorical traits. Effectiveness of classical and unknown parentage group procedures to estimate inbreeding coefficients was evaluated depending on completeness of a 5-generation pedigree. A novel method derived from the classical formula to estimate inbreeding was utilized to evaluate completeness of pedigrees. Two different estimates of maternal inbreeding were fitted in separate models as a linear covariate in combined LMM analyses (Holstein registered and grade cows and Jersey cows) or separate analyses (registered Holstein cows) by parity (1-4) with fetal inbreeding. Impact of inbreeding type, model, data structure, and treatment of herd-year-season (HYS) on magnitude and size of inbreeding depression were assessed. Grade Holstein datasets were sampled and analyzed by percentage of pedigree present (0-30%, 30-70% and 70-100%). BTM analyses (sire-mgs) were performed using Gibbs sampling for parities 1, 2 and 3 fitting maternal inbreeding only. In LMM analyses of grade data, the least pedigree and diagonal A matrix performed the worst. Significant inbreeding effects were obtained in most traits in cows of parity 1. Fetal inbreeding depression was mostly lower than that from maternal inbreeding. Inbreeding depression in binary traits was the most difficult to evaluate. Analyses with non-additive effects included in LMM, for data by inbreeding level and by age group should be preferred to estimate inbreeding depression. In BTM inbreeding effects were strongly related to dam parity and calf sex. Largest effects were obtained from parity 1 cows giving birth to male calves (0.417% and 0.252% for dystocia and calf mortality) and then births to female calves (0.300% and 0.203% for dystocia and calf mortality). Female calves from mature cows were the least affected (0.131% and 0.005% for dystocia and calf mortality). Data structure was found to be a very important factor to attainment of convergence in distribution. / Ph. D.

Communication is very common in human life. In fact, we take communication for granted and do not think about the challenges involved except when miscommunication happens. When two people communicate, information is exchanged. Each piece of information that comes through eliminates a series of structural choices an individual has available to him. According to Shannon‟s information theory, information reduces uncertainty by selecting one item from a set of possible items. That is Information distinguishes between relevant and irrelevant items in a set essentially dividing the set into two categories. Knowing also implies distinction or classification. The purpose of this thesis is to investigate the relationship between information and knowledge by observing what happens when people communicate to each other in an experimental context. The focus of our observation is on three main situations: – 1. What happens when people communicate to each other in the context of known categorical attributes; 2. What happens when people communicate in the context of unknown categorical attributes; and 3. How is the communication process affected in the presence of known but misleading attributes as, for instance, in a situation of a cross-functional communication in organization? By studying the interaction between pairs of participants, we propose that information and categorical knowledge relate to each other, as in a duality, and influence the communication process. The study comprises four experimental conditions. This thesis provides a description of the experimental conditions, a brief report on what happened during people‟s performance, as well as some preliminary findings based on observations.

Communication is very common in human life. In fact, we take communication for granted and do not think about the challenges involved except when miscommunication happens. When two people communicate, information is exchanged. Each piece of information that comes through eliminates a series of structural choices an individual has available to him. According to Shannon‟s information theory, information reduces uncertainty by selecting one item from a set of possible items. That is Information distinguishes between relevant and irrelevant items in a set essentially dividing the set into two categories. Knowing also implies distinction or classification. The purpose of this thesis is to investigate the relationship between information and knowledge by observing what happens when people communicate to each other in an experimental context. The focus of our observation is on three main situations: – 1. What happens when people communicate to each other in the context of known categorical attributes; 2. What happens when people communicate in the context of unknown categorical attributes; and 3. How is the communication process affected in the presence of known but misleading attributes as, for instance, in a situation of a cross-functional communication in organization? By studying the interaction between pairs of participants, we propose that information and categorical knowledge relate to each other, as in a duality, and influence the communication process. The study comprises four experimental conditions. This thesis provides a description of the experimental conditions, a brief report on what happened during people‟s performance, as well as some preliminary findings based on observations.

During this "white collar recession,'' there is a flooded labor market of workers. For employers seeking to hire, there is a need to identify potential qualified candidates for each job. The current state of the art is LinkedIn Recruiting or elastic search on Resumes. The current state of the art lacks efficiency and scalability along with an intuitive ranking of candidates. We believe this can be fixed with multi-layer categorical clustering via modularity maximization. To test this, we gathered a dataset that is extensive and representative of the job market. Our data comes from PeopleDataLabs and LinkedIn and is sampled from 153 million individuals. As such, this data represents one of the most informative datasets for the task of ranking and clustering job titles and skills. Properly grouping individuals will help identify more candidates to fulfill the multitude of vacant positions. We implement a novel framework for categorical clustering, involving these attributes to deliver a reliable pool of candidates. We develop a metric for clustering based on commonality to rank clustering algorithms. The metric prefers modularity-based clustering algorithms like the Louvain algorithm. This allows us to use such algorithms to outperform other unsupervised methods for categorical clustering. Our implementation accurately clusters emergency services, health-care and other fields while managerial positions are interestingly swamped by soft or uninformative features thereby resulting in dominant ambiguous clusters.

This thesis looks at characterising countably infinitely categorical theories. That is theories for which every countably infinite model is isomorphic to every other countably infinite model. The thesis looks at the Lindenbaum-Tarski algebra, Henkin theories, types and then ends with the Ryll-Nardzewski theorem which provides several equivalences to a theory being countably infinitely categorical.