Processing length bias of time intervals between the last episode and the interview in Gamma-Poisson models of behavior

Zelterman, Daniel; Суворова, Алена Владимировна; Пащенко, Антон Евгеньевич; Мусина, Валерия Фуатовна; Тулупьев, Александр Львович; Тулупьева, Татьяна Валентиновна; Krasnoselskikh, Т. V.; Гро, Лоретта E.; Хаймер, Роберт

doi:10.15622/sp.16.6

Cited by 2 publications

(1 citation statement)

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“…We can see that σ > 0 in represents a scale parameter and α > 0 controls the shape of this distribution. Zelterman et al also examine mathematical properties of this distribution. We display several examples of this density function in Figure for different values of α and σ in the range of values encountered among the fitted models of Section 4.…”

Section: Sampling Distribution and Marginal Data Analysismentioning

confidence: 99%

Beta prime regression with application to risky behavior frequency screening

Tulupyev

Суворова

Sousa

et al. 2013

Statistics in Medicine

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Our aim is to model the frequency of certain behavioral acts, especially those that are likely to transmit communicable diseases between persons. We develop a generalized linear model based on the beta prime distribution to model the responses to a survey question of the form, “When was the last time that you engaged in this behavior?” Intuitively, individuals reporting more recent events are more likely to have greater frequency of the risky behavior. The beta prime distribution is especially suited to this application because of its long tail. We adjust for length-biased sampling. We show how to use this distribution as the basis of a linear regression model that accounts for differences in demographic and psychological characteristics of the respondents. We discuss estimation of parameters, residuals, tests for heterogeneity of these parameters, and jackknife measures of influence. The methods are applied to a survey of alcohol abuse use among individuals who are at high risk for spreading HIV and other communicable diseases in a study conducted in St. Petersburg, Russia.

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Section: Sampling Distribution and Marginal Data Analysismentioning

confidence: 99%