Comparing the performance of the SF-6D and the EQ-5D in different patient groups

Introduction: This research aims to explore the performance of the SF-6D and the EQ-5D in patients suffering from asthma, chronic obstructive pulmonary disease, cataracts, and rheumatoid arthritis. In particular, the aim of this research is twofold: 1) to study the level of agreement between the indexes and the descriptive systems of the dimensions of the SF-6D and the EQ-5D, and 2) to analyze the discriminative ability of the instruments. Material and Methods: A sample of 643 patients completed both the SF-36v2 and the EQ-5D. The discriminative ability of the instruments was analyzed. Furthermore, the level of agreement between the indexes and the descriptive systems of the dimensions of the SF-6D and the EQ-5D were studied. The level of agreement between instruments was investigated using correlation coefficients and the Bland-Altman plots, while the influence of medical condition and other socio-demographic variables was analyzed using non-parametric tests. Paired-samples tests were used to identify differences between the scores. Results and Discussion: The results show a strong correlation and agreement between both indexes. Overall, questionnaire indexes differ by medical condition and socio-demographic groups and both instruments are able to discriminate between socio-demographic groups. Conclusion: This study confirmed the hypothesis that the SF-6D generates higher utility values in less healthy individuals. The SF-6D and the EQ-5D seem to perform differently in each of the diseases studied since the descriptive statistics differ between instruments and the level of correlation is not uniform. Results show that the instruments generate different utility values, but there is a strong agreement between both indexes. Thus, the two instruments are not interchangeable and their results cannot be directly comparable.Portuguese Foundation for Science and Technology (Fundacao para a Ciencia e a Tecnologia)info:eu-repo/semantics/publishedVersio

Estimation and forecasting in SUINAR(1) model

This work considers a generalization of the INAR(1) model to the panel data first order Seemingly Unrelated INteger AutoRegressive Poisson model, SUINAR(1). It presents Bayesian and classical methodologies to estimate the parameters of Poisson SUINAR(1) model and to forecast future observations of the process. In particular, prediction intervals for forecasts - classical approach - and HPD prediction intervals - Bayesian approach - are derived. A simulation study is provided to give additional insight into the finite sample behaviour of the parameter estimates and forecasts

INPE LANDSAT system

There are no author identified significant results in this report

Speech Recognition by Composition of Weighted Finite Automata

We present a general framework based on weighted finite automata and weighted finite-state transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including context-dependent units, pronunciation dictionaries, language models and lattices. Furthermore, general but efficient algorithms can used for combining information sources in actual recognizers and for optimizing their application. In particular, a single composition algorithm is used both to combine in advance information sources such as language models and dictionaries, and to combine acoustic observations and information sources dynamically during recognition.Comment: 24 pages, uses psfig.st

Beyond Word N-Grams

We describe, analyze, and evaluate experimentally a new probabilistic model for word-sequence prediction in natural language based on prediction suffix trees (PSTs). By using efficient data structures, we extend the notion of PST to unbounded vocabularies. We also show how to use a Bayesian approach based on recursive priors over all possible PSTs to efficiently maintain tree mixtures. These mixtures have provably and practically better performance than almost any single model. We evaluate the model on several corpora. The low perplexity achieved by relatively small PST mixture models suggests that they may be an advantageous alternative, both theoretically and practically, to the widely used n-gram models.Comment: 15 pages, one PostScript figure, uses psfig.sty and fullname.sty. Revised version of a paper in the Proceedings of the Third Workshop on Very Large Corpora, MIT, 199

Similarity-Based Models of Word Cooccurrence Probabilities

In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations ``eat a peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on ``most similar'' words. We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudo-word disambiguation. In the language modeling task, a similarity-based model is used to improve probability estimates for unseen bigrams in a back-off language model. The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error. We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easy-to-disambiguate high-frequency configurations. The similarity-based methods perform up to 40% better on this particular task.Comment: 26 pages, 5 figure