89 research outputs found
Augmented two-step estimating equations with nuisance functionals and complex survey data
Statistical inference in the presence of nuisance functionals with complex
survey data is an important topic in social and economic studies. The Gini
index, Lorenz curves and quantile shares are among the commonly encountered
examples. The nuisance functionals are usually handled by a plug-in
nonparametric estimator and the main inferential procedure can be carried out
through a two-step generalized empirical likelihood method. Unfortunately, the
resulting inference is not efficient and the nonparametric version of the
Wilks' theorem breaks down even under simple random sampling. We propose an
augmented estimating equations method with nuisance functionals and complex
surveys. The second-step augmented estimating functions obey the Neyman
orthogonality condition and automatically handle the impact of the first-step
plug-in estimator, and the resulting estimator of the main parameters of
interest is invariant to the first step method. More importantly, the
generalized empirical likelihood based Wilks' theorem holds for the main
parameters of interest under the design-based framework for commonly used
survey designs, and the maximum generalized empirical likelihood estimators
achieve the semiparametric efficiency bound. Performances of the proposed
methods are demonstrated through simulation studies and an application using
the dataset from the New York City Social Indicators Survey.Comment: 43 page
Combining Non-probability and Probability Survey Samples Through Mass Imputation
This paper presents theoretical results on combining non-probability and
probability survey samples through mass imputation, an approach originally
proposed by Rivers (2007) as sample matching without rigorous theoretical
justification. Under suitable regularity conditions, we establish the
consistency of the mass imputation estimator and derive its asymptotic variance
formula. Variance estimators are developed using either linearization or
bootstrap. Finite sample performances of the mass imputation estimator are
investigated through simulation studies and an application to analyzing a
non-probability sample collected by the Pew Research Centre.Comment: Submitted to Journal of the Royal Statistical Society: Series
TrimTail: Low-Latency Streaming ASR with Simple but Effective Spectrogram-Level Length Penalty
In this paper, we present TrimTail, a simple but effective emission
regularization method to improve the latency of streaming ASR models. The core
idea of TrimTail is to apply length penalty (i.e., by trimming trailing frames,
see Fig. 1-(b)) directly on the spectrogram of input utterances, which does not
require any alignment. We demonstrate that TrimTail is computationally cheap
and can be applied online and optimized with any training loss or any model
architecture on any dataset without any extra effort by applying it on various
end-to-end streaming ASR networks either trained with CTC loss [1] or
Transducer loss [2]. We achieve 100 200ms latency reduction with equal
or even better accuracy on both Aishell-1 and Librispeech. Moreover, by using
TrimTail, we can achieve a 400ms algorithmic improvement of User Sensitive
Delay (USD) with an accuracy loss of less than 0.2.Comment: submitted to ICASSP 202
Electronic properties and 4f→ 5d transitions in Ce-doped Lu2SiO5: a theoretical investigation
This is an electronic version of an article published in Journal of Materials Chemistry. Ning, L., Lin, L., Li, L., Wu, C., Duan, C., Zhang, Y. and Luis Seijo. "Electronic properties and 4f 5d transitions in Ce-doped Lu2SiO5: a theoretical investigation". Journal of Materials Chemistry 22 (2012): 13723-1373
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