Many medical studies collect biomarker data to gain insight into the biological mechanisms underlying both acute and chronic diseases. These markers may be obtained at a single point in time to aid in the diagnosis of an illness or may be collected longitudinally to provide information on the...

Since the formulation by Black, Scholes, and Merton in 1973 of the first rational option pricing formula which depended only on observable values, the volume of options traded daily on the Chicago Board of Exchange has grown rapidly. In fact, over the past three decades, options have undergone a...

Longitudinal and repeated measurement data commonly arise in many scientific researchareas. Traditional methods have focused on estimating single mean response as a function ofa time related variable and other covariates in a homogeneous population. However, in manysituations the homogeneity...

The proliferation of many clinical studies obtaining multiple biophysical signals from several individuals repeatedly in time is increasingly recognized, a recognition generating growth in statistical models that analyze cross-sectional time series data. In general, these statistical models try...

The main concern of financial time series analysis is how to forecast future values of financialvariables, based on all available information. One of the special features of financial variables,such as stock prices and exchange rates, is that they show changes in volatility, or variance,over...

This simulation study compared the performance of two multilevel measurement testlet (MMMT) models: Beretvas and Walker’s (2008) two-level MMMT model and Jiao, Wang, and Kamata’s (2005) three-level model. Several conditions were manipulated (including testlet length, sample size, and the...

Discrete longitudinal data are common in various disciplines and are often used to assess the change over time of one or several outcomes, and/or what covariates might be associated with the outcomes. Existing parametric and nonparametric/semiparametric models typically attribute the...

This Monte Carlo study examined the relative performance of four missing data treatment (MDT) approaches applied to incomplete cross-sectional hierarchical data: maximum likelihood (ML) estimation, multiple imputation under a normal model (MI/NM), multiple imputation under a linear mixed model...