Zobrazeno 1 - 10
of 144
pro vyhledávání: '"MAO Xiaojun"'
Publikováno v:
Shanghai yufang yixue, Vol 34, Iss 1, Pp 56-59 (2022)
ObjectiveTo investigate the status of maternal iodine deficiency in Lishui City of Zhejiang Province， and explore the effect of maternal iodine deficiency on the growth and development of infants.MethodsA total of 209 pregnant women living in Liand
Externí odkaz:
https://doaj.org/article/21d423d894ec43989430265bed658618
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following diff
Externí odkaz:
http://arxiv.org/abs/2402.03954
In recent years, privacy-preserving machine learning algorithms have attracted increasing attention because of their important applications in many scientific fields. However, in the literature, most privacy-preserving algorithms demand learning obje
Externí odkaz:
http://arxiv.org/abs/2401.01294
The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. However, constructing such an estimator involves estimating the high-dimensional inverse Hessian matrix, incurring significant computational cost
Externí odkaz:
http://arxiv.org/abs/2306.10395
Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. Moreover, transduction with matrix completion is a useful method in multi-label learning. In this paper, we propose a transduct
Externí odkaz:
http://arxiv.org/abs/2302.09834
Nonresponse frequently arises in practice, and simply ignoring it may lead to erroneous inference. Besides, the number of collected covariates may increase as the sample size in modern statistics, so parametric imputation or propensity score weightin
Externí odkaz:
http://arxiv.org/abs/2209.13855
Privacy-preserving data analysis has become more prevalent in recent years. In this study, we propose a distributed group differentially private Majority Vote mechanism, for the sign selection problem in a distributed setup. To achieve this, we apply
Externí odkaz:
http://arxiv.org/abs/2209.04419
Non-probability sampling is prevailing in survey sampling, but ignoring its selection bias leads to erroneous inferences. We offer a unified nonparametric calibration method to estimate the sampling weights for a non-probability sample by calibrating
Externí odkaz:
http://arxiv.org/abs/2204.09193
Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications. To obtain the robust and sparse estimators, a natural idea is to adopt the non-smooth median loss combined with a $\ell_
Externí odkaz:
http://arxiv.org/abs/2202.05498
Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and useless features. Herein, we propose a nonparametric feature selection algo
Externí odkaz:
http://arxiv.org/abs/2201.06821