Recurrent Events Models

Introduction: Various psychosocial studies on health related lifestyles lay emphasis on the fact that the
perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive
behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between
eight independent variables and one dependent variable to isolate predictors which have significant influence on
behavior and sexual practices.
Methods: A Cross-sectional design was used for the study. Structured close-ended interviewer-administered
questionnaire was used to collect primary data. Multistage stratified technique was used to sample views from
380 students from Takoradi Polytechnic, Ghana. A Hierarchical multiple regression model was used to ascertain
the significance of certain predictors of sexual behavior and practices.
Results: The variables that were extracted from the multiple regression were; for the constant;  =14.202,
t=2.279, p=0.023, variable is significant; for the marital status;  =0.092, t=1.996, p<0.05, variable is
significant; for the knowledge on AIDs;  = 0.090, t=1.996, p<0.05, variable is significant; for the attitude
towards HIV/AIDs;  =0.486, t=10.575, p<0.001, variable is highly significant. Thus, the best fitting model for
predicting behavior and sexual practices was a linear combination of the constant, one’s marital status,
knowledge on HIV/AIDs and Attitude towards HIV/AIDs.,
0 1 2
3
( ) ( ) ( / )
( / ).
Y Behavior and sexual practices Marital status Knowledge on HIV AIDs issues
Attitude towards HIV AIDs issues
  

  

β0, β1, β2 and β3 are respectively 14.201, 2.038, 0.148 and 0.486; the higher the better.
Conclusions: Attitude and behavior change education on HIV/AIDs should be intensified in the institution so
that students could adopt better lifestyles.
Keywords: attitudes, hierarchical regressions, knowledge, sex education, sexual practices

Introduction: Various psychosocial studies on health related lifestyles lay emphasis on the fact that the perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between eight independent variables and one dependent variable to isolate predictors which have significant influence on behavior and sexual practices.

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We propose a general class of semiparametric transformation models with random effects to formulate the effects of possibly time-dependent covari- ates on clustered or correlated failure times. This class encompasses all commonly used transformation models, including proportional hazards and proportional odds models, and it accommodates a variety of random-effects distributions, particularly Gaussian distributions. We show that the nonparametric maximum likelihood

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Recognizing non-recurrent events in short-term traffic flow evolution is of ultimate importance in advanced transportation systems. The present paper proposes a methodological framework for the recognition and prediction of complex and irregular traffic flow patterns; an advanced nonlinear analysis is conducted in order to recognize patterns with respect to the statistical characteristics of their evolution. Following, patterns are predicted using a genetically-optimized probabilistic neural network. Results indicate the existence of three basic types of temporal patterns: (a) stable, (b) unstable, (c) non-recurrent (unique). Moreover, the developed probabilistic network provides accurate one-step ahead pattern predictions. The proposed model infers knowledge on the occurrence of patterns and transitions and can be a significant source of information in order to improve on short-term predictability of traffic flow.

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