Exact computation of GMM estimators for instrumental variable quantile regression models

We show that the generalized method of moments (GMM) estimation problem in instrumental variable quantile regression (IVQR) models can be equivalently formulated as a mixed integer quadratic programming problem. This enables exact computation of the GMM estimators for the IVQR models. We illustrate the usefulness of our algorithm via Monte Carlo experiments and an application to demand for fish

Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities. Exploring the semiparametric modeling restrictions, we show that the identified set can be equivalently formulated by moment inequalities conditional on only two continuous indexing variables. Such formulation holds regardless of the covariate dimension, thereby breaking the curse of dimensionality for nonparametric inference based on the underlying conditional moment inequalities. We further apply this dimension reducing characterization approach to the monotone single index model and to a variety of semiparametric models under which the sign of conditional expectation of a certain transformation of the outcome is the same as that of the indexing variable

Spontaneous Formation of Stable Capillary Bridges for Firming Compact Colloidal Microstructures in Phase Separating Liquids: A Computational Study

Computer modeling and simulations are performed to investigate capillary bridges spontaneously formed between closely packed colloidal particles in phase separating liquids. The simulations reveal a self-stabilization mechanism that operates through diffusive equilibrium of two-phase liquid morphologies. Such mechanism renders desired microstructural stability and uniformity to the capillary bridges that are spontaneously formed during liquid solution phase separation. This self-stabilization behavior is in contrast to conventional coarsening processes during phase separation. The volume fraction limit of the separated liquid phases as well as the adhesion strength and thermodynamic stability of the capillary bridges are discussed. Capillary bridge formations in various compact colloid assemblies are considered. The study sheds light on a promising route to in-situ (in-liquid) firming of fragile colloidal crystals and other compact colloidal microstructures via capillary bridges

Dynamic Voids Surrounded by Shocked Conventional Polytropic Gas Envelopes

With proper physical mechanisms of energy and momentum input from around the centre of a self-gravitating polytropic gas sphere, a central spherical "void" or "cavity" or "bubble" of very much less mass contents may emerge and then dynamically expand into a variety of surrounding more massive gas envelopes with or without shocks. We explore self-similar evolution of a self-gravitating polytropic hydrodynamic flow of spherical symmetry with such an expanding "void" embedded around the center. The void boundary supporting a massive envelope represents a pressure-balanced contact discontinuity where drastic changes in mass density and temperature occur. We obtain numerical void solutions that can cross the sonic critical surface either smoothly or by shocks. Using the conventional polytropic equation of state, we construct global void solutions with shocks travelling into various envelopes including static polytropic sphere, outflow, inflow, breeze and contraction types. In the context of supernovae, we discuss the possible scenario of separating a central collapsing compact object from an outgoing gas envelope with a powerful void in dynamic expansion. Initially, a central bubble is carved out by an extremely powerful neutrinosphere. After the escape of neutrinos during the decoupling, the strong electromagnetic radiation field and/or electron-positron pair plasma continue to drive the cavity expansion. In a self-similar dynamic evolution, the pressure across the contact discontinuity decreases with time to a negligible level for a sufficiently long lapse and eventually, the gas envelope continues to expand by inertia. We describe model cases of polytropic index $\gamma=4/3-\epsilon$ with $\epsilon>0$ and discuss pertinent requirements to justify our proposed scenario.Comment: 20 pages, 12 Figures. Accepted for publication in MNRA

Biochemical prevention and treatment of viral infections – A new paradigm in medicine for infectious diseases

For two centuries, vaccination has been the dominating approach to develop prophylaxis against viral infections through immunological prevention. However, vaccines are not always possible to make, are ineffective for many viral infections, and also carry certain risk for a small, yet significant portion of the population. In the recent years, FDA's approval and subsequent market acceptance of Synagis, a monoclonal antibody indicated for prevention and treatment of respiratory syncytial virus (RSV) has heralded a new era for viral infection prevention and treatment. This emerging paradigm, herein designated "Biochemical Prevention and Treatment", currently involves two aspects: (1) preventing viral entry via passive transfer of specific protein-based anti-viral molecules or host cell receptor blockers; (2) inhibiting viral amplification by targeting the viral mRNA with anti-sense DNA, ribozyme, or RNA interference (RNAi). This article summarizes the current status of this field

Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions

In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology, an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions

Non-negative matrix factorization for self-calibration of photometric redshift scatter in weak lensing surveys

Photo-z error is one of the major sources of systematics degrading the accuracy of weak lensing cosmological inferences. Zhang et al. (2010) proposed a self-calibration method combining galaxy-galaxy correlations and galaxy-shear correlations between different photo-z bins. Fisher matrix analysis shows that it can determine the rate of photo-z outliers at a level of 0.01-1% merely using photometric data and do not rely on any prior knowledge. In this paper, we develop a new algorithm to implement this method by solving a constrained nonlinear optimization problem arising in the self-calibration process. Based on the techniques of fixed-point iteration and non-negative matrix factorization, the proposed algorithm can efficiently and robustly reconstruct the scattering probabilities between the true-z and photo-z bins. The algorithm has been tested extensively by applying it to mock data from simulated stage IV weak lensing projects. We find that the algorithm provides a successful recovery of the scatter rates at the level of 0.01-1%, and the true mean redshifts of photo-z bins at the level of 0.001, which may satisfy the requirements in future lensing surveys.Comment: 12 pages, 6 figures. Accepted for publication in ApJ. Updated to match the published versio

Maximum Score Estimation of Preference Parameters for a Binary Choice Model under Uncertainty

This paper develops maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectations. The preference parameters are estimated in two stages: we estimate conditional expectations nonparametrically in the first stage and then the preference parameters in the second stage based on Manski (1975, 1985)'s maximum score estimator using the choice data and first stage estimates. The paper establishes consistency and derives rate of convergence of the two-stage maximum score estimator. Moreover, the paper also provides sufficient conditions under which the two-stage estimator is asymptotically equivalent in distribution to the corresponding single-stage estimator that assumes the first stage input is known. These results are of independent interest for maximum score estimation with nonparametrically generated regressors. The paper also presents some Monte Carlo simulation results for finite-sample behavior of the two-stage estimator