Weighted pairwise likelihood estimation for a general class of random effects models

Models with random effects/latent variables are widely used for capturing unobserved heterogeneity in multilevel/hierarchical data and account for associations in multivariate data. The estimation of those models becomes cumbersome as the number of latent variables increases due to high-dimensional integrations involved. Composite likelihood is a pseudo-likelihood that combines lower-order marginal or conditional densities such as univariate and/or bivariate; it has been proposed in the literature as an alternative to full maximum likelihood estimation. We propose a weighted pairwise likelihood estimator based on estimates obtained from separate maximizations of marginal pairwise likelihoods. The derived weights minimize the total variance of the estimated parameters. The proposed weighted estimator is found to be more efficient than the one that assumes all weights to be equal. The methodology is applied to a multivariate growth model for binary outcomes in the analysis of four indicators of schistosomiasis before and after drug administration

Use of the Lagrange multiplier test for assessing measurement invariance under model misspecification

This article studies the Type I error, false positive rates, and power of four versions of the Lagrange multiplier test to detect measurement noninvariance in item response theory (IRT) models for binary data under model misspecification. The tests considered are the Lagrange multiplier test computed with the Hessian and cross-product approach, the generalized Lagrange multiplier test and the generalized jackknife score test. The two model misspecifications are those of local dependence among items and nonnormal distribution of the latent variable. The power of the tests is computed in two ways, empirically through Monte Carlo simulation methods and asymptotically, using the asymptotic distribution of each test under the alternative hypothesis. The performance of these tests is evaluated by means of a simulation study. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the tests performance deteriorates, especially for false positive rates under local dependence and power for small sample size under misspecification of the latent variable distribution. In general, the Lagrange multiplier test computed with the Hessian approach and the generalized Lagrange multiplier test have better performance in terms of false positive rates while the Lagrange multiplier test computed with the cross-product approach has the highest power for small sample sizes. The asymptotic power turns out to be a good alternative to the classic empirical power because it is less time consuming. The Lagrange tests studied here have been also applied to a real data set

The asymptotic power of the Lagrange multiplier tests for misspecified IRT models

This article studies the power of the Lagrange Multiplier Test and the Generalized Lagrange Multiplier Test to detect measurement non-invariance in Item Response Theory (IRT) models for binary data. We study the performance of these two tests under correct model specification and incorrect distribution of the latent variable. The asymptotic distribution of each test under the alternative hypothesis depends on a noncentrality parameter that is used to compute the power. We present two different procedures to compute the noncentrality parameter and consequently the power of the tests. The performance of the two methods is evaluated through a simulation study. They turn out to be very similar to the classic empirical power but less time consuming. Moreover, the results highlight that the Lagrange Multiplier Test is more powerful than the Generalized Lagrange Multiplier Test to detect measurement non-invariance under all simulation conditions

Speed up Zig-Zag

The Zig-Zag process is a piecewise deterministic Markov process, efficiently used for simulation in an MCMC setting. As we show in this article, it fails to be exponentially ergodic on heavy tailed target distributions. We introduce an extension of the Zig-Zag process by allowing the process to move with a nonconstant speed function s, depending on the current state of the process. We call this process Speed Up Zig-Zag (SUZZ). We provide conditions that guarantee stability properties for the SUZZ process, including nonexplosivity, exponential ergodicity in heavy tailed targets and central limit theorem. Interestingly, we find that using speed functions that induce explosive deterministic dynamics may lead to stable algorithms that can even mix faster. We further discuss the choice of an efficient speed function by providing an efficiency criterion for the one-dimensional process and we support our findings with simulation results

Microfluidic assays for DNA manipulation based on a block copolymer immobilization strategy

Methods to manipulate and visualize isolated DNA and oligonucleotide strands are important for investigation of their biophysics as well as their interactions with proteins. Herein, we report such a method by combining a block copolymer surface functionalization strategy with microfluidics. The copolymer poly(L-lysine-graftpolyethylene glycol) (PLL-g-PEG) coated one surface of the microfluidic channels, rendering it passive to adsorption and thus minimizing any noise arising from nontargeted adsorbed molecules. Single λ-phage DNA molecules were immobilized and were extended by molecular combing. Their extension did not exceed their contour length, which we attribute to the low surface tension of the coated surface. To demonstrate further the applicability of our method, the anchored DNA was extended by hydrodynamic flow. We propose this method for exploring DNA-protein interactions due to the copolymer's enhanced capacity for single-molecule detection, stability under wet or dry conditions, hydrophilicity, full compatibility with microfluidics and simplicity being a one-step process. © 2010 American Chemical Society

Low threshold edge emitting polymer distributed feedback laser based on a square lattice

Copyright © 2005 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Applied Physics Letters 86 (2005) and may be found at http://link.aip.org/link/?APPLAB/86/161102/1We report the demonstration of a low-threshold, edge-emitting polymer distributed feedback laser based on a square lattice. The lattice constant was 268 nm, which corresponds to a lattice line spacing in the ΓM symmetry direction of the Brillouin zone of 189 nm. The latter was employed to provide feedback at 630 nm via a first order diffraction process. The device operated on two longitudinal modes, which were situated on the band-edge near the M symmetry point. The two modes had thresholds of 0.66 nJ and 1.2 nJ—significantly lower than comparable surface-emitting DFB lasers. Angle dependent photoluminescence experiments were performed to investigate the effect of the square lattice on the laser operation and the origin of the low threshold