Adjusted empirical likelihood with high-order precision

Empirical likelihood is a popular nonparametric or semi-parametric statistical method with many nice statistical properties. Yet when the sample size is small, or the dimension of the accompanying estimating function is high, the application of the empirical likelihood method can be hindered by low precision of the chi-square approximation and by nonexistence of solutions to the estimating equations. In this paper, we show that the adjusted empirical likelihood is effective at addressing both problems. With a specific level of adjustment, the adjusted empirical likelihood achieves the high-order precision of the Bartlett correction, in addition to the advantage of a guaranteed solution to the estimating equations. Simulation results indicate that the confidence regions constructed by the adjusted empirical likelihood have coverage probabilities comparable to or substantially more accurate than the original empirical likelihood enhanced by the Bartlett correction.Comment: Published in at http://dx.doi.org/10.1214/09-AOS750 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comparative study of pre⁃column derivatization liquid chromatography and post-column derivatization liquid chromatography for the determination of free formaldehyde residues in vaccines

Objective: To establish a pre⁃column derivatization liquid chromatography method and a post⁃column derivatization liquid chromatography method for the determination of residual amount of free formaldehyde in vaccines. Consistency of the results of two methods was investigated. Methods: The pre⁃column derivatization liq⁃ uid chromatography was performed on a Shimadzu LC⁃20AT liquid chromatograph (SPD⁃20A UV detector). Sepa⁃ ration was accomplished on a Kromasil 100⁃5⁃C18(250mm×4.6mm) column with a mobile phase of 60% acetoni⁃ trile solution at a flow rate of 0. 8 mL·min-1 at 40℃ and the detection wavelength was 360 nm. The post⁃column derivatization liquid chromatography was performed on a Shimadzu LC⁃20AT liquid chromatograph (SPD⁃M20A diode array detector and vector derivative device). Separation was accomplished on a Chrom Core AQ⁃C18 (250mm ×4.6mm) column with a mobile phase of 0.2% (V/V) phosphoric acid solution at a flow rate of 1.0 mL·min-1 at 25℃ and the detection wavelength was 412 nm. The derivatization solution was acetate buffer, the flow rate was 0. 5 mL·min-1, and the temperature was 100℃. The precision, repeatability and sample recovery of the two methods were investigated, and the experiment results were tested for significance by F⁃test and t⁃test. Results: The precolumn derivatization liquid chromatography had good linearity in the range of 0. 025 -100μg· mL-1(R =0. 999 9, n =12). RSD values of precision and repeatability were 0. 06 % and 0.3%-1.4%, respectively. The average recoveries were 97. 3%-104. 8 % with RSD of 0. 7%-2. 9 %. The limit of quantitation was 0. 02μg·mL-1, and the limit of detection was 0. 01μg·mL-1. The post⁃column derivatization liquid chromatog⁃ raphy had good linearity in the range of 0.025-100μg·mL-1 (R =0. 9999, n=12). RSD values of precision and repeatability were 0. 02% and 0. 0.7%-3.5 %, respectively. The average recoveries were 105. 6%-114. 6% with RSD of 0.3% - 1.9%. The limit of quantitation was 0. 02μg·mL-1, and the limit of detection was 0. 006μg·mL-1. The F⁃test and the t⁃test results showed there was no significant difference between two methods. Conclusion: Two methods are simple and accurate with high sensitivity and good specificity, which can be applicable to the determination of free formaldehyde residues in vaccines

3D indoor scene modeling from RGB-D data: a survey

3D scene modeling has long been a fundamental problem in computer graphics and computer vision. With the popularity of consumer-level RGB-D cameras, there is a growing interest in digitizing real-world indoor 3D scenes. However, modeling indoor 3D scenes remains a challenging problem because of the complex structure of interior objects and poor quality of RGB-D data acquired by consumer-level sensors. Various methods have been proposed to tackle these challenges. In this survey, we provide an overview of recent advances in indoor scene modeling techniques, as well as public datasets and code libraries which can facilitate experiments and evaluation

Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach

Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based rate-compatible LDPC ecoding network, which utilizes the structure of raptor-like LDPC codes and can deal with multiple code rates. In the proposed network, different portions of parameters are activated to deal with distinct code rates, which leads to parameter sharing among tasks. Numerical experiments demonstrate the effectiveness of the proposed method. Training the specially designed network under multiple code rates makes the decoder compatible with multiple code rates without sacrificing frame error rate performance