While much public attention and considerable controversy surround pharmaceutical marketing practices and their impact on physicians, views on the matter have largely been shaped by anecdotal evidence or results from analyses with insufficient controls. Making use of a dynamic fixed effects distributed lag regression model, we empirically assess the role that two central components of pharmaceut...

This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These models include systems of equations with limited dependent variables and unobserved individual effects, and sample selection models with unobserved individual effects. Our two-step approach first estimates the reduced form by fixed effects procedures to ...

Image fusion is a fundamental technique for integrating high-resolution panchromatic images and low-resolution multispectral (MS) images. Fused images may enhance image interpretation. Empirical mode decomposition (EMD) is an effective method of decomposing non-stationary signals into a set of intrinsic mode functions (IMFs). Hence, the characteristics of EMD may apply to image fusion technique...

We discuss the evolution of cosmological gamma-ray burst remnants, consisting of the cooling and expanding fireball ejecta together with any swept-up external matter, after the gamma-ray event. We show that significant optical emission is predicted which should be measurable for timescales of hours after the event, and in some cases radio emission may be expected days to weeks after the event. ...

New concepts in the manipulation of time in video editing are presented, enabling some unique visual effects. These effects include (i) slowing down (or postponing) some dynamic events while speeding up (or advancing) others. (ii) Creating dynamic panoramic images of dynamic scenes. (iii) Generating new views of a scene without a 3D model. (iv) Creating panoramic stereo images. Time manipulatio...

This paper complements existing power analysis tools by offering tools to compute minimum detectable effect sizes (MDES) for existing studies and to estimate minimum required sample sizes (MRSS) for studies under design. The tools that accompany this paper support estimates of MDES or MSSR for 21 different study designs that include 14 random assignment designs (6 designs in which individuals a...

The existing techniques of forecasting a future count either treat the time series of counts as a Gaussian time series or use a random effects based dynamic Poisson model. The normality based approach may not yield valid forecasting, whereas the random effects based model usually generates a complex correlation structure for the time series of counts which may be impractical to use for forecast...

Latent class models with crossed subject-specific and test(rater)-specific random effects have been proposed to estimate the diagnostic accuracy (sensitivity and specificity) of a group of binary tests or binary ratings. However, the computation of these models are hindered by their complicated Monte Carlo Expectation–Maximization (MCEM) algorithm. In this article, a class of pseudo-likelihood ...

This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and shape variation is captured by adding random effects to the segmental model. The resulting probabilistic framework provides a basis for learning of waveform models from data as well as parsing and rec...

This paper develops computationally feasible methods for estimating random effects models in the context of regression modelling of multiple independent time series of discrete valued counts in which there is serial dependence. Given covariates, random effects and process history, the observed responses at each time in each series are independent and have an exponential family distribution. We ...