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Truncation (statistics)
About: Truncation (statistics) is a research topic. Over the lifetime, 1816 publications have been published within this topic receiving 43861 citations.
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5,063 citations
TL;DR: The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences and weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs.
Abstract: The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have been based on weighted estimation of the parameters of marginal structural models because, unlike standard statistical methods, weighting can appropriately adjust for measured time-varying confounders affected by prior exposure. As an example, the authors describe the last three assumptions using the change in viral load due to initiation of antiretroviral therapy among 918 human immunodeficiency virus-infected US men and women followed for a median of 5.8 years between 1996 and 2005. The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences. For instance, a tradeoff between bias and precision is illustrated as a function of the extent to which confounding is controlled. Weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs. Inverse probability weighting provides a powerful methodological tool that may uncover causal effects of exposures that are otherwise obscured. However, as with all methods, diagnostics and sensitivity analyses are essential for proper use.
2,071 citations
TL;DR: In this paper, a simple algorithm is constructed and shown to converge monotonically to yield a maximum likelihood estimate of a distribution function when the data are incomplete due to grouping, censoring and/or truncation.
Abstract: SUMMARY This paper is concerned with the non-parametric estimation of a distribution function F, when the data are incomplete due to grouping, censoring and/or truncation. Using the idea of self-consistency, a simple algorithm is constructed and shown to converge monotonically to yield a maximum likelihood estimate of F. An application to hypothesis testing is indicated.
1,669 citations
TL;DR: Tobin's model is also known as censored or truncated regression models as discussed by the authors, where the observations outside a specified range are totally lost and censored if one can at least observe the exogenous variables, and truncation occurs if a patient is still alive at the last observation date or if he or she cannot be located.
1,552 citations
TL;DR: It is shown that a deterministic relationship between the truncation lag and the sample size is dominated by data-dependent rules that take sample information into account, and methods based on sequential tests over those based on informat...
Abstract: We analyze the choice of the truncation lag in the context of the Said-Dickey test for the presence of a unit root in a general autoregressive moving average model. It is shown that a deterministic relationship between the truncation lag and the sample size is dominated by data-dependent rules that take sample information into account. In particular, we study data-dependent rules that are not constrained to satisfy the lower bound condition imposed by Said-Dickey. Akaike's information criterion falls into this category. The analytical properties of the truncation lag selected according to a class of information criteria are compared to those based on sequential testing for the significance of coefficients on additional lags. The asymptotic properties of the unit root test under various methods for selecting the truncation lag are analyzed, and simulations are used to show their distinctive behavior in finite samples. Our results favor methods based on sequential tests over those based on informat...
1,427 citations