Language independent on-off voice over IP source model with lognormal transitions

The recent explosive growth of voice over IP (VoIP) solutions calls for accurate modelling of VoIP traffic. This study presents measurements of ON and OFF periods of VoIP activity from a significantly large database of VoIP call recordings consisting of native speakers speaking in some of the world's most widely spoken languages. The impact of the languages and the varying dynamics of caller interaction on the ON and OFF period statistics are assessed. It is observed that speaker interactions dominate over language dependence which makes monologue-based data unreliable for traffic modelling. The authors derive a semi-Markov model which accurately reproduces the statistics of composite dialogue measurements. © The Institution of Engineering and Technology 2013.

Application of the D-Fullness Technique for Breakdown Point Study of the Trimmed Likelihood Estimator to a Generalized Logistic Model

2000 Mathematics Subject Classification: 62J12, 62F35

Implementation of the EM Algorithm for Maximum Likelihood Estimation of a Random Effects Model for One Longitudinal Ordinal Outcome

2010 Mathematics Subject Classification: 62J99.

One Voice, Two Attitudes: Perceptions of Spanish and English among Cuban Americans in Miami , Lecture by Phillip M. Carter

This flyer promotes the event "One Voice, Two Attitudes: Perceptions of Spanish and English among Cuban Americans in Miami Lecture by Phillip M. Carter" hosted by the Cuban Research Institute.

Enhancing physical therapy students' attitudes toward telecommunication technology by using a demonstration model

The purpose of this study was to assess and enhance the attitudes and knowledge of physical therapy students toward telecommunication technology. A questionnaire was given to appraise the attitudes and knowledge of 156 physical therapy students toward telecommunication technology. The intervention was a one hour presentation on applications relevant to physical therapy practice. The majority of students expressed interest in telecommunication before the presentation, and felt that expanded use of telecommunication was important to the profession. However, only a minority of students demonstrated knowledge about specific medical telecommunication applications. The post-intervention questionnaire showed the presentation to be effective in changing students' attitudes toward telecommunication, and increasing their knowledge relevant to the practice of physical therapy. If physical therapy curricula were to include exposure to telecommunication, perhaps physical therapists will be more inclined to use the technology in the future.

Empirical likelihood based longitudinal studies

In longitudinal data analysis, our primary interest is in the regression parameters for the marginal expectations of the longitudinal responses; the longitudinal correlation parameters are of secondary interest. The joint likelihood function for longitudinal data is challenging, particularly for correlated discrete outcome data. Marginal modeling approaches such as generalized estimating equations (GEEs) have received much attention in the context of longitudinal regression. These methods are based on the estimates of the first two moments of the data and the working correlation structure. The confidence regions and hypothesis tests are based on the asymptotic normality. The methods are sensitive to misspecification of the variance function and the working correlation structure. Because of such misspecifications, the estimates can be inefficient and inconsistent, and inference may give incorrect results. To overcome this problem, we propose an empirical likelihood (EL) procedure based on a set of estimating equations for the parameter of interest and discuss its characteristics and asymptotic properties. We also provide an algorithm based on EL principles for the estimation of the regression parameters and the construction of a confidence region for the parameter of interest. We extend our approach to variable selection for highdimensional longitudinal data with many covariates. In this situation it is necessary to identify a submodel that adequately represents the data. Including redundant variables may impact the model’s accuracy and efficiency for inference. We propose a penalized empirical likelihood (PEL) variable selection based on GEEs; the variable selection and the estimation of the coefficients are carried out simultaneously. We discuss its characteristics and asymptotic properties, and present an algorithm for optimizing PEL. Simulation studies show that when the model assumptions are correct, our method performs as well as existing methods, and when the model is misspecified, it has clear advantages. We have applied the method to two case examples.

Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.

OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.

WHEN INTERACTING ONLINE LEADS TO TAKING ACTION OFFLINE: EXAMINING THE EFFECTS OF ONLINE PERSUASION ON IS USERS’ PRO-ENVIRONMENTAL BEHAVIOURS

With the increasing attention towards the role of information systems (IS) as a vehicle to address environmental issues, IS researchers and practitioners have strived to leverage advanced Green IS innovations to persuade people to engage in environmentally responsible practices and support pro-environmental initiatives. Yet, existing research reveals that the persuasion effects of Green IS designs remain equivocal. In particular, many design characteristics advocated in Green IS research can produce bi-directional changes in IS users’ attitudes and behaviours. To address this issue, this thesis drew upon the circumplex model of social values (S.H. Schwartz, 1992) to explain when and how online persuasion designs come to affect people’s judgements on resource conservation and environmental protection. Three sets of working propositions and specific hypotheses were developed. Specifically, this research suggests that the use of an IS application can elicit different value primes and draw IS users’ attentions to different motivational functions of engaging in suggested behavioural changes. It is expected that matching online persuasion appeals with IS users’ personal value priorities can increase users’ acceptance of online behavioural suggestions. Second, it is hypothesized that the persuasion effect tends to be weakened, as the system users become aware of the valuematching design in a given IS application. Third, it is proposed that different value primes presented in an IS application can result in different unintended effects on IS users’ global pro-environmental attitudes and motivations. The hypotheses were tested in the two pilot studies and two full-scale online experiments. The study findings generally support the main predictions of the hypotheses. On the one hand, this thesis providesiii empirical evidence that IS design for online persuasion can be instrumental in influencing IS users’ judgements on a range of resource conservation practices. On the other hand, this work explains why the effectiveness of IS-enabled online persuasion attempts needs to be measured not only in terms of the intended changes in a target behavioural domain but also in terms of unintended changes in people’s general environmental orientations. Findings in this research may bring a different perspective on understanding and assessing the influence of Green IS applications on IS users’ judgements and behaviou

The Biopsychosocial Spiritual Model Applied To The Treatment Of women With Breast Cancer, Through Rime Intervention (relaxation, mental Images, Spirituality).

This postdoctoral study on the application of the RIME intervention in women that had undergone mastectomy and were in treatment, aimed to promote psychospiritual and social transformations to improve the quality of life, self-esteem and hope. A total of 28 women participated and were randomized into two groups. Brief Psychotherapy (PB) (average of six sessions) was administered in the Control Group, and RIME (three sessions) and BP (average of five sessions) were applied in the RIME Group. The quantitative results indicated a significant improvement (38.3%) in the Perception of Quality of Life after RIME according to the WHOQOL, compared both to the BP of the Control Group (12.5%), and the BP of the RIME Group (16.2%). There was a significant improvement in Self-esteem (Rosenberg) after RIME (14.6%) compared to the BP of the Control Group (worsened 35.9%), and the BP of the RIME Group (8.3%). The improvement in well-being, considering the focus worked on (Visual Analog Scale), was significant in the RIME Group (bad to good), as well as in the Control Group (unpleasant to good). The qualitative results indicated that RIME promotes creative transformations in the intrapsychic and interpersonal dimensions, so that new meanings and/or new attitudes emerge into the consciousness. It was observed that RIME has more strength of psychic structure, ego strengthening and provides a faster transformation that BP, therefore it can be indicated for crisis treatment in the hospital environment.

Adaptação e validação da escla ASKAS - Aging Sexual Knowledge and Attitudes Scale em idosos brasileiros

Universidade Estadual de Campinas . Faculdade de Educação Física

Richards growth model and viability indicators for populations subject to interventions

In this work we study the problem of modeling identification of a population employing a discrete dynamic model based on the Richards growth model. The population is subjected to interventions due to consumption, such as hunting or farming animals. The model identification allows us to estimate the probability or the average time for a population number to reach a certain level. The parameter inference for these models are obtained with the use of the likelihood profile technique as developed in this paper. The identification method here developed can be applied to evaluate the productivity of animal husbandry or to evaluate the risk of extinction of autochthon populations. It is applied to data of the Brazilian beef cattle herd population, and the the population number to reach a certain goal level is investigated.

Generalized Sznajd model for opinion propagation

In the last decade the Sznajd model has been successfully employed in modeling some properties and scale features of both proportional and majority elections. We propose a version of the Sznajd model with a generalized bounded confidence rule-a rule that limits the convincing capability of agents and that is essential to allow coexistence of opinions in the stationary state. With an appropriate choice of parameters it can be reduced to previous models. We solved this model both in a mean-field approach (for an arbitrary number of opinions) and numerically in a Barabaacutesi-Albert network (for three and four opinions), studying the transient and the possible stationary states. We built the phase portrait for the special cases of three and four opinions, defining the attractors and their basins of attraction. Through this analysis, we were able to understand and explain discrepancies between mean-field and simulation results obtained in previous works for the usual Sznajd model with bounded confidence and three opinions. Both the dynamical system approach and our generalized bounded confidence rule are quite general and we think it can be useful to the understanding of other similar models.

Coexistence of interacting opinions in a generalized Sznajd model

The Sznajd model is a sociophysics model that mimics the propagation of opinions in a closed society, where the interactions favor groups of agreeing people. It is based in the Ising and Potts ferromagnetic models and, although the original model used only linear chains, it has since been adapted to general networks. This model has a very rich transient, which has been used to model several aspects of elections, but its stationary states are always consensus states. In order to model more complex behaviors, we have, in a recent work, introduced the idea of biases and prejudices to the Sznajd model by generalizing the bounded confidence rule, which is common to many continuous opinion models, to what we called confidence rules. In that work we have found that the mean field version of this model (corresponding to a complete network) allows for stationary states where noninteracting opinions survive, but never for the coexistence of interacting opinions. In the present work, we provide networks that allow for the coexistence of interacting opinions for certain confidence rules. Moreover, we show that the model does not become inactive; that is, the opinions keep changing, even in the stationary regime. This is an important result in the context of understanding how a rule that breeds local conformity is still able to sustain global diversity while avoiding a frozen stationary state. We also provide results that give some insights on how this behavior approaches the mean field behavior as the networks are changed.

A note on the likelihood and moments of the skew-normal distribution

In this paper an alternative approach to the one in Henze (1986) is proposed for deriving the odd moments of the skew-normal distribution considered in Azzalini (1985). The approach is based on a Pascal type triangle, which seems to greatly simplify moments computation. Moreover, it is shown that the likelihood equation for estimating the asymmetry parameter in such model is generated as orthogonal functions to the sample vector. As a consequence, conditions for a unique solution of the likelihood equation are established, which seem to hold in more general setting.

A log-extended Weibull regression model

A bathtub-shaped failure rate function is very useful in survival analysis and reliability studies. The well-known lifetime distributions do not have this property. For the first time, we propose a location-scale regression model based on the logarithm of an extended Weibull distribution which has the ability to deal with bathtub-shaped failure rate functions. We use the method of maximum likelihood to estimate the model parameters and some inferential procedures are presented. We reanalyze a real data set under the new model and the log-modified Weibull regression model. We perform a model check based on martingale-type residuals and generated envelopes and the statistics AIC and BIC to select appropriate models. (C) 2009 Elsevier B.V. All rights reserved.