Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors

This paper considers identification and inference of a general latent nonlinear model using two samples, where a covariate contains arbitrary measurement errors in both samples, and neither sample contains an accurate measurement of the corresponding true variable. The primary sample consists of some dependent variables, some error-free covariates and an error-ridden covariate, where the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values. The auxiliary sample consists of another noisy measurement of the mismeasured covariate and some error-free covariates. We first show that a general latent nonlinear model is nonparametrically identified using the two samples when both could have nonclassical errors, with no requirement of instrumental variables nor independence between the two samples. When the two samples are independent and the latent nonlinear model is parameterized, we propose sieve quasi maximum likelihood estimation (MLE) for the parameter of interest, and establish its root-n consistency and asymptotic normality under possible misspecification, and its semiparametric efficiency under correct specification. We also provide a sieve likelihood ratio model selection test to compare two possibly misspecified parametric latent models. A small Monte Carlo simulation and an empirical example are presented.Data combination, Nonlinear errors-in-variables model, Nonclassical measurement error, Nonparametric identification, Misspecified parametric latent model, Sieve likelihood estimation and inference

Nonparametric identification and estimation of nonclassical errors-in-variables models without additional information

This paper considers identification and estimation of a nonparametric regression model with an unobserved discrete covariate. The sample consists of a dependent variable and a set of covariates, one of which is discrete and arbitrarily correlates with the unobserved covariate. The observed discrete covariate has the same support as the unobserved covariate, and can be interpreted as a proxy or mismeasure of the unobserved one, but with a nonclassical measurement error that has an unknown distribution. We obtain nonparametric identification of the model given monotonicity of the regression function and a rank condition that is directly testable given the data. Our identification strategy does not require additional sample information, such as instrumental variables or a secondary sample. We then estimate the model via the method of sieve maximum likelihood, and provide root-n asymptotic normality and semiparametric efficiency of smooth functionals of interest. Two small simulations are presented to illustrate the identification and the estimation results.

Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments

This note considers nonparametric identification of a general nonlinear regression model with a dichotomous regressor subject to misclassification error. The available sample information consists of a dependent variable and a set of regressors, one of which is binary and error-ridden with misclassification error that has unknown distribution. Our identification strategy does not parameterize any regression or distribution functions, and does not require additional sample information such as instrumental variables, repeated measurements, or an auxiliary sample. Our main identifying assumption is that the regression model error has zero conditional third moment. The results include a closed-form solution for the unknown distributions and the regression function.

Spatial patterns of correlation between conspecific species and size diversity in forest ecosystems

Recently correlations between spatial species and size diversity have been found in many forest ecosystems around the world. They are likely to play a prominent role in nature's mechanisms of maintaining species and size diversity. In this study, we analysed the species population means of spatial species-mingling and sizeinequality indices in 36 large forest monitoring plots from the temperate and subtropical zones in China. Based on the literature we included eleven diversity-index combinations and considered their correlations for increasing numbers of nearest neighbours. Generally, positive correlations are related to between-species population size differences whilst negative correlations reflect within-species population size differences. Our results showed that the selected species-mingling and size-inequality indices produced different correlation patterns in one and the same monitoring site. We therefore defined a species-mingling size-inequality correlation space by computing the 0.025 and the 0.975 quantiles from the correlation data of the eleven index combinations. We noticed that each observed correlation space included 1-3 combinations of five basic geometric types and can be interpreted as the unique signature of a forest ecosystem in time. The correlation space allowed us to understand more clearly at which spatial scale within-species correlation was more influential than between-species inequality and vice versa. The shape of the correlation space is interpretable and gives important clues about the forest development stage of a forest ecosystem

Quantum oscillations in adsorption energetics of atomic oxygen on Pb(111) ultrathin films: A density-functional theory study

Using first-principles calculations, we have systematically studied the quantum size effects of ultrathin Pb(111) films on the adsorption energies and diffusion energy barriers of oxygen atoms. For the on-surface adsorption of oxygen atoms at different coverages, all the adsorption energies are found to show bilayer oscillation behaviors. It is also found that the work function of Pb(111) films still keeps the bilayer-oscillation behavior after the adsorption of oxygen atoms, with the values being enlarged by 2.10 to 2.62 eV. For the diffusion and penetration of the adsorbed oxygen atoms, it is found that the most energetically favored paths are the same on different Pb(111) films. And because of the modulation of quantum size effects, the corresponding energy barriers are all oscillating with a bilayer period on different Pb(111) films. Our studies indicate that the quantum size effect in ultrathin metal films can modulate a lot of processes during surface oxidation

A pH-Sensitive Injectable Nanoparticle Composite Hydrogel for Anticancer Drug Delivery

According to previous reports, low pH-triggered nanoparticles were considered to be excellent carriers for anticancer drug delivery, for the reason that they could trigger encapsulated drug release at mild acid environment of tumor. Herein, an acid-sensitive β-cyclodextrin derivative, namely, acetalated-β-cyclodextrin (Ac-β-CD), was synthesized by acetonation and fabricated to nanoparticles through single oil-in-water (o/w) emulsion technique. At the same time, camptothecin (CPT), a hydrophobic anticancer drug, was encapsulated into Ac-β-CD nanoparticles in the process of nanoparticle fabrication. Formed nanoparticles exhibited nearly spherical structure with diameter of 209±40 nm. The drug release behavior of nanoparticles displayed pH dependent changes due to hydrolysis of Ac-β-CD. In order to overcome the disadvantages of nanoparticle and broaden its application, injectable hydrogels with Ac-β-CD nanoparticles were designed and prepared by simple mixture of nanoparticles solution and graphene oxide (GO) solution in this work. The injectable property was confirmed by short gelation time and good mobility of two precursors. Hydrogels were characterized by dynamic mechanical test and SEM, which also reflected some structural features. Moreover, all hydrogels underwent a reversible sol-gel transition in alkaline environment. Finally, the results of in vitro drug release profile indicated that hydrogel could control drug release or bind drug inside depending on the pH value of released medium