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Inverse probability weighting (IPW) is widely used in many areas when data are subject to unrepresentativeness, missingness, or selection bias. An inevitable challenge with the use of IPW is that the IPW estimator can be remarkably unstable if some probabilities are very close to zero. To overcome this problem, at least three remedies have been developed in the literature: stabilizing, thresholding, and trimming. However the final estimators are still IPW type estimators, and inevitably inherit certain weaknesses of the naive IPW estimator: they may still be unstable or biased. We propose a biased-sample empirical likelihood weighting (ELW) method to serve the same general purpose as IPW, while completely overcoming the instability of IPW-type estimators by circumventing the use of inverse probabilities. The ELW weights are always well defined and easy to implement. We show theoretically that the ELW estimator is asymptotically normal and more efficient than the IPW estimator and its stabilized version for missing data problems and unequal probability sampling without replacement. Its asymptotic normality is also established under unequal probability sampling with replacement. Our simulation results and a real data analysis indicate that the ELW estimator is shift-equivariant, nearly unbiased, and usually outperforms the IPW-type estimators in terms of mean square error., Comment: 37 pages, 4 figures

Ferromagnet/heavy metal (FM/HM) multilayer thin films with $C_{2v}$ symmetry have the potential to host antiskyrmions and other chiral spin textures via an anisotropic Dzyaloshinkii-Moriya interaction (DMI). Here, we present a candidate material system that also has a strong uniaxial magnetocrystalline anisotropy aligned in the plane of the film. This system is based on a new Co/Pt epitaxial relationship, which is the central focus of this work: hexagonal closed-packed Co$(10\bar{1}0)[0001]$ $\parallel$ face-centered cubic Pt$(110)[001]$. We characterized the crystal structure and magnetic properties of our films using X-ray diffraction techniques and magnetometry respectively, including q-scans to determine stacking fault densities and their correlation with the measured magnetocrystalline anisotropy constant and thickness of Co. In future ultrathin multilayer films, we expect this epitaxial relationship to further enable an anisotropic DMI while supporting interfacial perpendicular magnetic anisotropy. The anticipated confluence of these properties, along with the tunability of multilayer films, make this material system a promising testbed for unveiling new spin configurations in FM/HM films., Comment: 5 pages, 6 figures

Missing data are frequently encountered in various disciplines and can be divided into three categories: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Valid statistical approaches to missing data depend crucially on correct identification of the underlying missingness mechanism. Although the problem of testing whether this mechanism is MCAR or MAR has been extensively studied, there has been very little research on testing MAR versus MNAR.A critical challenge that is faced when dealing with this problem is the issue of model identification under MNAR. In this paper, under a logistic model for the missing probability, we develop two score tests for the problem of whether the missingness mechanism is MAR or MNAR under a parametric model and a semiparametric location model on the regression function. The score tests require only parameter estimation under the null MAR assumption, which completely circumvents the identification issue. Our simulations and analysis of human immunodeficiency virus data show that the score tests have well-controlled type I errors and desirable powers., Comment: 22 pages, 4 tables, 2 figures

Recently neural architecture search(NAS) has been successfully used in image classification, natural language processing, and automatic speech recognition(ASR) tasks for finding the state-of-the-art(SOTA) architectures than those human-designed architectures. NAS can derive a SOTA and data-specific architecture over validation data from a pre-defined search space with a search algorithm. Inspired by the success of NAS in ASR tasks, we propose a NAS-based ASR framework containing one search space and one differentiable search algorithm called Differentiable Architecture Search(DARTS). Our search space follows the convolution-augmented transformer(Conformer) backbone, which is a more expressive ASR architecture than those used in existing NAS-based ASR frameworks. To improve the performance of our method, a regulation method called Dynamic Search Schedule(DSS) is employed. On a widely used Mandarin benchmark AISHELL-1, our best-searched architecture outperforms the baseline Conform model significantly with about 11% CER relative improvement, and our method is proved to be pretty efficient by the search cost comparisons., Comment: submitted to INTERSPEECH 2021

In multi-center clinical trials, due to various reasons, the individual-level data are strictly restricted to be assessed publicly. Instead, the summarized information is widely available from published results. With the advance of computational technology, it has become very common in data analyses to run on hundreds or thousands of machines simultaneous, with the data distributed across those machines and no longer available in a single central location. How to effectively assemble the summarized clinical data information or information from each machine in parallel computation has become a challenging task for statisticians and computer scientists. In this paper, we selectively review some recently-developed statistical methods, including communication efficient distributed statistical inference, and renewal estimation and incremental inference, which can be regarded as the latest development of calibration information methods in the era of big data. Even though those methods were developed in different fields and in different statistical frameworks, in principle, they are asymptotically equivalent to those well known methods developed in meta analysis. Almost no or little information is lost compared with the case when full data are available. As a general tool to integrate information, we also review the generalized method of moments and estimating equations approach by using empirical likelihood method., Comment: 37 pages

Consider a population consisting of clusters of sampling units, evolving temporally, spatially, or according to other dynamics. We wish to monitor the evolution of its means, medians, or other parameters. For administrative convenience and informativeness, clustered data are often collected via a rotating plan. Under rotating plans, the observations in the same clusters are correlated, and observations on the same unit collected on different occasions are also correlated. Ignoring this correlation structure may lead to invalid inference procedures. Accommodating cluster structure in parametric models is difficult or will have a high level of misspecification risk. In this paper, we explore exchangeability in clustered data collected via a rotating sampling plan to develop a permutation scheme for testing various hypotheses of interest. We also introduce a semiparametric density ratio model to facilitate the multiple population structure in rotating sampling plans. The combination ensures the validity of the inference methods while extracting maximum information from the sampling plan. A simulation study indicates that the proposed tests firmly control the type I error whether or not the data are clustered. The use of the density ratio model improves the power of the tests., Comment: 26 pages

During the past few decades, missing-data problems have been studied extensively, with a focus on the ignorable missing case, where the missing probability depends only on observable quantities. By contrast, research into non-ignorable missing data problems is quite limited. The main difficulty in solving such problems is that the missing probability and the regression likelihood function are tangled together in the likelihood presentation, and the model parameters may not be identifiable even under strong parametric model assumptions. In this paper we discuss a semiparametric model for non-ignorable missing data and propose a maximum full semiparametric likelihood estimation method, which is an efficient combination of the parametric conditional likelihood and the marginal nonparametric biased sampling likelihood. The extra marginal likelihood contribution can not only produce efficiency gain but also identify the underlying model parameters without additional assumptions. We further show that the proposed estimators for the underlying parameters and the response mean are semiparametrically efficient. Extensive simulations and a real data analysis demonstrate the advantage of the proposed method over competing methods., Comment: 45 pages