Modeling longitudinal data in acute illness

Modeling longitudinal data in acute illness

Biomarkers of sepsis could allow early identification of high-risk patients, in whom aggressive interventions can be life-saving. Among those interventions are the immunomodulatory therapies, which will hopefully become increasingly available to clinicians. However, optimal use of such interventions will probably be patient specific and based on longitudinal profiles of such biomarkers. Modelin...

modeling loss data by phase-type distribution

بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...

Modeling Incomplete Longitudinal Data

Conditional Dependence in Longitudinal Data Analysis

Mixed models are widely used to analyze longitudinal data. In their conventional formulation as linear mixed models (LMMs) and generalized LMMs (GLMMs), a commonly indispensable assumption in settings involving longitudinal non-Gaussian data is that the longitudinal observations from subjects are conditionally independent, given subject-specific random effects. Although conventional Gaussian...

Functional Modeling of Longitudinal Data

SUMMARY Functional data analysis provides an inherently nonparametric approach for the analysis of data which consist of samples of time courses or random trajectories. It is a relatively young field aiming at modeling and data exploration under very flexible model assumptions with no or few parametric components. Basic tools of functional data analysis are smoothing, functional principal compo...