This article proposes a procedure for small sample regression, systematically using the concept of robust Bayesian inference and a contaminated prior. The approach explores the possible domain of population informatio...
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ISBN:
(纸本)9781467389860
This article proposes a procedure for small sample regression, systematically using the concept of robust Bayesian inference and a contaminated prior. The approach explores the possible domain of population information and attempts to estimate regression parameters further. A data augmentation step included in the procedure works to enlarge the original small data set by adding new data to it. It follows that when the expectation-maximization (em) algorithm is used to output the hypothesis, approximating the true (but unobservable) parameters based on the enlarged data set. Both the augmented data set and the maximum likelihood estimate used are generated, based on the implementation of contaminated priors. The experiments show that the proposed procedure can effectively lower the mean squared error when modeling.
Medical treatment quality assessment of hospital is becoming more and more important for the public. The severity of illness is a significant variable to assess the treatment quality of hospital, and it would be serio...
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Medical treatment quality assessment of hospital is becoming more and more important for the public. The severity of illness is a significant variable to assess the treatment quality of hospital, and it would be seriously challenging for the assessment if the severity of illness is unobserved. We propose a new widely applicable hierarchical model to make assessment by treating the severity as a latent variable. Moreover, we could simultaneously assess cost efficiency, treatment efficiency and join efficiency which combines the fi rst two efficiency. As an illustration, we estimate the efficiency on a pneumonia dataset and obtain convincing results.
作者：
Waled KhaledJin-guan LinSchool of Mathematics
Southeast University Nanjing P. R. China Department of Applied Statistics Damascus University Syria School of Mathematics
Southeast University Nanjing P. R. China School of Statistics and Mathematics Nanjing Audit University Nanjing P.R. China
The single-index model is a semi-parametric regression model that avoids the curse of dimensionality because of the linear combination of p-regression coefficients and covariates. Most of the works in this setting don...
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ISBN:
(纸本)9781450376617
The single-index model is a semi-parametric regression model that avoids the curse of dimensionality because of the linear combination of p-regression coefficients and covariates. Most of the works in this setting done for the homogenous single index models are limited and based on the minimum average conditional variance estimation (MAVE). To overcome these drawbacks, in this paper, we provide a robust and efficient estimate with modal regression for the single-index model under the existence of heteroscedasticity. The em algorithm and bandwidth selection are employed to prepare the estimation method. Simulation studies demonstrate the performance of the proposed estimation; this method outperforms MAVE in various situations even if the errors are generated from a heavy-tailed distribution while it achieves the same efficiency as well as MAVE for the normally distributed errors. Finally, the application of the proposed method is illustrated by a real example of the heteroscedastic model.
The normal-inverse model arises as a normal variancemean mixture with an inverse Gaussian mixing model. The resulting model, it is very complicated to obtain the influence measures based on the tradition method. In...
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ISBN:
(纸本)9781424470815;9780769540474
The normal-inverse model arises as a normal variancemean mixture with an inverse Gaussian mixing model. The resulting model, it is very complicated to obtain the influence measures based on the tradition method. In the present paper, several diagnostic measures for outlier data mining are obtained based on the conditional expectation of the complete-data loglikelihood function based on the em algorithm. An example for which we apply the diagnosis methods is given as illustration.
Airport drop-off service provided by airlines is a chauffeur-driven service(i.e. Uber and Di Di) as an emerging travel choice for travelers. More and more passenger enjoy the drop-off service. In practice, we find an ...
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ISBN:
(纸本)9781509012572
Airport drop-off service provided by airlines is a chauffeur-driven service(i.e. Uber and Di Di) as an emerging travel choice for travelers. More and more passenger enjoy the drop-off service. In practice, we find an interesting question: if a passenger has ever choice the drop-off service, whether they are willing to recommend this service to other traveler? Although the acknowledgment that social learning is related to travel decision is promoted, quantitative analysis about how social learning shape and impact the decision of passengers is still limited. We study and estimate a diffusion probability between different passengers by proposing a CCM(Co-travel Link Cascade Model) based on a modified em iterative algorithm. Then, we segment passengers into three types(Influenced, Unchecked and Immune). The three types of passengers are predicated by approaches of IC-like model, Random Forest model and probabilistic model, respectively. In addition, we also design a parallel implementation of our proposed algorithm in the Apache Spark distributed data processing environment. Experimental results on a real aviation data set demonstrate that CCM can efficiently infer the decision of travelers.
Understanding how a society views certain policies, politicians, and events can help shape public policy, legislation, and even a political candidate's campaign. This paper focuses on using aggregated, or interval...
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Understanding how a society views certain policies, politicians, and events can help shape public policy, legislation, and even a political candidate's campaign. This paper focuses on using aggregated, or interval censored, polling data to estimate the times when the public opinion shifts on the US president's job approval. The approval rate is modelled as a Poisson segmented (joinpoint) regression with the em algorithm used to estimate the model parameters. Inference on the change points is carried out using BIC based model averaging. This approach can capture the uncertainty in both the number and location of change points. The model is applied to president Trump's job approval rating during 2020. Three primary change points are discovered and related to significant events and statements.
In this paper, we propose a novel online speech dereverberation with multichannel microphone input signals for noisy environments. Unlike conventional dereverberation methods which optimizes the dereverberation filter...
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ISBN:
(纸本)9781479988518
In this paper, we propose a novel online speech dereverberation with multichannel microphone input signals for noisy environments. Unlike conventional dereverberation methods which optimizes the dereverberation filter by noisy microphone input signals, the proposed method optimizes the dereverberation filter by noiseless microphone input signals so as to achieve a good dereverberation filter under noisy environments. Noiseless microphone input signals are estimated by multichannel Wiener filtering which can be interpreted as combination of multichannel beamforming and time-varying single-channel Wiener filtering. In multichannel Wiener filtering, residual reverberation which cannot be reduced by the time-invariant dereverberation filter is also reduced. Optimization of the parameters are updated by using the expectation-maximization algorithm in an on-line manner. Experimental results show that the proposed method can reduce reverberation and background noise effectively in an on-line manner even when microphone input signals are observed under noisy enviornments.
This paper proposes a sparse source separation method which clusters the phase difference between the microphone observations and the amplitude modulation (AM) of the source spectrum simultaneously. The phase differen...
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ISBN:
(纸本)9781424442959
This paper proposes a sparse source separation method which clusters the phase difference between the microphone observations and the amplitude modulation (AM) of the source spectrum simultaneously. The phase difference clustering separates the signals in each frequency bin, and the AM clustering corresponds to permutation alignment. Because the proposed method has an inherent ability to align the permutation of frequency components, the proposed method can be applied even when the spatial aliasing problem occurs. Moreover, because the common AM property collects the synchronized frequency components, we can model the microphone observations with a small number of sources. This property enables us to count the number of sources. That is, the proposed method can be applied even if the number of sources is unknown. The experimental results confirm the effectiveness of our proposed method.
This paper proposed a new method of image registration based on clustering *** used clustering algorithm to cluster all the feature vectors of images,and adopted em algorithm to optimize the parameters and *** result ...
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This paper proposed a new method of image registration based on clustering *** used clustering algorithm to cluster all the feature vectors of images,and adopted em algorithm to optimize the parameters and *** result shows that the proposed image registration method can improve the precise of image registration,and reduce error.
The OFDM systems with the serial concatenated convolutional codes(SCCC)interleave the information random in the time field(COFDM).It is able to overcome the abrupt noises and enhance the performance of the whole syste...
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The OFDM systems with the serial concatenated convolutional codes(SCCC)interleave the information random in the time field(COFDM).It is able to overcome the abrupt noises and enhance the performance of the whole system *** in the actual communication systems,the frequency offset deteriorates the performance of COFDM *** this problem becomes more complex,because the frequency offset is relative to the difference of frequency between transmitter and receiver and the Doppler shifts of channel those are *** paper combines the MAP decoding algorithm of SCCC with the expectation-maximization(em)algorithm of tracking frequency offset(MAP-em),and uses the feedback iterative technique to implement the estimate of both the frequency offset and *** technique can enhance the performance of systems,because it bases on the ML and the information could be helped each other between the OFDM *** simulations show that the more iterative the better performance of the tracking precision and the BER of whole systems.
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