Semiparametric efficiency in GMM models with auxiliary data

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database, when such distribution is common to the two data sets. The auxiliary sample can be independent of the primary sample, or can be a subset of it. For both cases, we derive bounds when the probability of missing data given the proxy variables is unknown, or known, or belongs to a correctly specified parametric family. We find that the conditional probability is not ancillary when the two samples are independent. For all cases, we discuss efficient semiparametric estimators. An estimator based on a conditional expectation projection is shown to require milder regularity conditions than one based on inverse probability weighting.Comment: Published in at http://dx.doi.org/10.1214/009053607000000947 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general nonlinear, possibly non-smooth and over-identified moment restrictions, where the sampling information consists of a primary sample and an auxiliary sample. The variables of interest in the moment conditions are not directly observable in the primary data set, but the primary data set contains proxy variables which are correlated with the variables of interest. The auxiliary data set contains information about the conditional distribution of the variables of interest given the proxy variables. Identification is achieved by the assumption that this conditional distribution is the same in both the primary and auxiliary data sets. We provide semiparametric efficiency bounds for both the "verify-out-of-sample" case, where the two samples are independent, and the "verify-in-sample" case, where the auxiliary sample is a subset of the primary sample; and the bounds are derived when the propensity score is unknown, or known, or belongs to a correctly specified parametric family. These efficiency variance bounds indicate that the propensity score is ancillary for the "verify-in-sample" case, but is not ancillary for the "verify-out-of-sample" case. We show that sieve conditional expectation projection based GMM estimators achieve the semiparametric efficiency bounds for all the above mentioned cases, and establish their asymptotic efficiency under mild regularity conditions. Although inverse probability weighting based GMM estimators are also shown to be semiparametrically efficient, they need stronger regularity conditions and clever combinations of nonparametric and parametric estimates of the propensity score to achieve the efficiency bounds for various cases. Our results contribute to the literature on non-classical measurement error models, missing data and treatment effects.Auxiliary data, Measurement error, Missing data, Treatment effect, Semiparametric efficiency bound, GMM, Sieve estimation

Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general nonlinear, possibly non-smooth and over-identified moment restrictions, where the sampling information consists of a primary sample and an auxiliary sample. The variables of interest in the moment conditions are not directly observable in the primary data set, but the primary data set contains proxy variables which are correlated with the variables of interest. The auxiliary data set contains information about the conditional distribution of the variables of interest given the proxy variables. Identification is achieved by the assumption that this conditional distribution is the same in both the primary and auxiliary data sets. We provide semiparametric efficiency bounds for both the “verify-out-of-sample” case, where the two samples are independent, and the “verify-in-sample” case, where the auxiliary sample is a subset of the primary sample; and the bounds are derived when the propensity score is unknown, or known, or belongs to a correctly specified parametric family. These efficiency variance bounds indicate that the propensity score is ancillary for the “verify-in-sample” case, but is not ancillary for the “verify-out-of-sample” case. We show that sieve conditional expectation projection based GMM estimators achieve the semiparametric efficiency bounds for all the above mentioned cases, and establish their asymptotic efficiency under mild regularity conditions. Although inverse probability weighting based GMM estimators are also shown to be semiparametrically efficient, they need stronger regularity conditions and clever combinations of nonparametric and parametric estimates of the propensity score to achieve the efficiency bounds for various cases. Our results contribute to the literature on non-classical measurement error models, missing data and treatment effects

Sequence analysis reveals mosaic genome of Aichi virus

Aichi virus is a positive-sense and single-stranded RNA virus, which demonstrated to be related to diarrhea of Children. In the present study, phylogenetic and recombination analysis based on the Aichi virus complete genomes available in GenBank reveal a mosaic genome sequence [GenBank: FJ890523], of which the nt 261-852 region (the nt position was based on the aligned sequence file) shows close relationship with AB010145/Japan with 97.9% sequence identity, while the other genomic regions show close relationship with AY747174/German with 90.1% sequence identity. Our results will provide valuable hints for future research on Aichi virus diversity

Experimental study of the Couple Characteristics of the Refrigerants and Vortex Tube

Vortex tube is a simple energy separation device, also known as Ranque tube or Hilsch tube, which can separate a high-pressure stream into two different hot and cold streams. Since its simple structure and unique temperature separation characteristics, vortex tube has been widely used in various industries. In recent years, with the in-depth study of the vortex tube, it has been found that compared with the conventional expansion expander and the throttle valve, the vortex tube is much more structurally simple and efficient, respectively. Researchers have proposed the use of the vortex tube in the refrigeration system in order to reduce the throttling loss and improve system efficiency. This has important implications for improving the performance of the system, to achieve energy saving and emission reduction. However, due to the different physical properties of the different working fluid, energy separation in the vortex tube are not the same. In the existing studies on the vortex tube, the working fluid mainly used air, nitrogen, carbon dioxide and other natural refrigerants, the research about the influence of refrigerants is few. Due to the fact that the vortex tube is increasingly used in refrigeration and heating system, it is urgent to study the coupling characteristics between vortex tube and refrigerants and find optimal conditions in different systems. The different temperature separation effect of the refrigerants in the vortex tube in the low inlet pressure(300kPa) have been studied in our previous study and three fluid characteristics (specific heat ratio, kinematic viscosity, thermal conductivity) were considered as main influencing factors of energy separation. The influence of different working fluid in high pressure conditions has not been considered ,which is part of research work in this paper. The coupling characteristic between vortex tube and refrigerants wais studied and the closed loop system was constructed. R134a, R744, R32, R227ea were selected as the working fluids, experiments were carried out in different inlet pressure(500kPa?850kPa), different inlet temperature (308.15K?333.15K), different cold flow ratio (20%?97%). The temperature separation of different working fluids under different conditions were explored and the influences of different characteristics of the working fluids on the temperature separation process were discussed. These studies can help more profound understanding of the vortex tube temperature separation process, and also has certain significance on the applications of the vortex tube in the refrigeration system

Open-Cell Metal Foams for Use in Dehumidifying Heat Exchangers

Metal foams with an open-cell structure have physical and mechanical properties suggesting they might have advantages over conventional fin materials for use in air-cooling heat exchangers. For example, metal foams are low weight, have high to very high specific surface area (up to over 10000 m2/m3), have high gas permeability, and have relatively high thermal conductivity (for open-cell bodies). Due to these properties, open-cell metal foams have been studied for many heat transfer applications, especially as a material for constructing efficient compact heat exchangers. In this work, dynamic dips tests are undertaken to explore the water-drainage behavior of the metal foams. Experiments are also undertaken in a closed-loop wind tunnel to evaluate the pressure drop and heat transfer performance of metal foam heat exchangers under dry- and wet-surface conditions

Relationship between Perceived Social Support and Quality of Life among Kidney Transplant Recipients

Objective The aim of the study was to explore the relationship between perceived social support and quality of life (QOL) among recipients after kidney transplantation. Methods 210 kidney transplant recipients participated in this survey, and survey tools included the Multidimensional Scale of Perceived Social Support (MSPSS) and the Medical Outcomes Study 36-item Short Form (SF-36). Results The mean scores of kidney transplant recipients for MSPSS three sub-scales family support, friend support, and significant others support were 6.18±0.90, 5.48±1.32, 5.65±1.05 respectively, while MSPSS total scale score was 5.77±0.91. The mean scores for SF-36 Physical Component Summary (PCS) and Mental Component Summary (MCS) were 47.48±6.70, 48.40±9.65 respectively. Recipients’ PCS scores were correlated to significant others support sub-scale score and MSPSS total score significantly (P<0.05), while MCS scores were correlated to three sub-scales scales and total scale score (P<0.01). Conclusion Perceived social support of patients after kidney transplantation was significant related to their quality of life. The higher perceived social support was associated with the better quality of life