We analyze the changing attitudes toward immigration in EU host countries in the last few years (2010-2018) on the basis of the European Social Survey data. These data are collected by the administration of a question...
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We analyze the changing attitudes toward immigration in EU host countries in the last few years (2010-2018) on the basis of the European Social Survey data. These data are collected by the administration of a questionnaire made of items concerning different aspects related to the immigration phenomenon. For this analysis, we rely on a latent class approach considering a variety of models that allow for: (1) multidimensionality;(2) discreteness of the latent trait distribution;(3) time-constant and time-varying covariates;and (4) sample weights. Through these models we find latent classes of Europeans with similar levels of immigration acceptance and we study the effect of different socio-economic covariates on the probability of belonging to these classes for which we provide a specific interpretation. In this way we show which countries tend to be more or less positive toward immigration and we analyze the temporal dynamics of the phenomenon under study.
Students' engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A pre-requirement for ...
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Students' engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A pre-requirement for stimulating student engagement lies in the capability to have an approximate representation model for comprehending students' varied (dis)engagement behaviors. In this paper, we utilized model-based clustering for this purpose which generates K mixture Markov models to group students' traces containing their (dis)engagement behavioral patterns. To prevent the expectation-maximization (EM) algorithm from getting stuck in a local maxima, we also introduced a K-means-based initialization method named as K-EM. We performed an experimental work on two real datasets using the three variants of the EM algorithm the original EM, emEM, K-EM;and, non-mixture baseline models for both datasets. The proposed K-EM has shown very promising results and achieved significant performance difference in comparison with the other approaches particularly using the Dataset 1. Hence, we suggest to perform further experiments using large dataset(s) to validate our method. Additionally, visualization of the resultant clusters through first-order Markov chains reveals very useful insights about (dis)engagement behaviors depicted by the students. We conclude the paper with a discussion on the usefulness of our approach, limitations and potential extensions of this work.
In this paper we introduce a multivariate family of distributions for multivariate count data with excess zeros, which is a multivariate extension of the univariate zero-inflated Bell distribution. We derive various g...
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In this paper we introduce a multivariate family of distributions for multivariate count data with excess zeros, which is a multivariate extension of the univariate zero-inflated Bell distribution. We derive various general properties of this multivariate distribution. In particular, the marginal distributions are univariate zero-inflated Bell distributions. The model parameters are estimated using the traditional maximum likelihood estimation method. In addition, we develop a simple EM algorithm to compute the maximum likelihood estimates of the parameters of the new multivariate distribution with closed-form expressions for the maximum likelihood estimators. Empirical applications that employ real multivariate count data are considered to illustrate the usefulness of the new class of multivariate distributions, and comparisons with the multivariate zero-inflated Poisson distribution, multivariate zero-adjusted Poisson distributions, and multivariate zero-inflated generalized Poisson distribution are made. (c) 2021 Elsevier Inc. All rights reserved.
Passenger vehicles are increasingly adopting the use of automated driving systems (ADS) to help ease the workload of drivers and to improve road safety. These systems require human drivers to constantly maintain super...
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ISBN:
(纸本)9781665476331
Passenger vehicles are increasingly adopting the use of automated driving systems (ADS) to help ease the workload of drivers and to improve road safety. These systems require human drivers to constantly maintain supervisory control of the ADS. For safe adoption and ADS, the attention or alertness of the driver needs to be continuously monitored. Past studies have demonstrated pupil dilation as an effective measure of cognitive load. However, the raw pupil data recorded using eye trackers are noisy which may result in poor classification of the cognitive load levels of the driver. In this paper, an approach to reduce the noise raw pupil size data obtained from eye trackers used by ADS is proposed. The proposed approach uses a Kalman filter to filter out high-frequency noise that arises due to sudden changes in ambient light, head/body movement, and measurement noise. Data collected from 16 participants were used to demonstrate the performance of the model-based pupil-size filtering approach presented in this paper. Results show an objective improvement in the potential to distinguish changes in pupil size due to various levels of cognitive workload experienced by participants.
The Ising model is valuable in examining complex interactions within a system, but its estimation is challenging. In this work, we proposed penalized likelihood procedures to infer conditional dependence structure whe...
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The Ising model is valuable in examining complex interactions within a system, but its estimation is challenging. In this work, we proposed penalized likelihood procedures to infer conditional dependence structure when observed data come from heterogeneous resources in high-dimensional setting. The proposed method can be efficiently implemented by taking advantage of coordinate-ascent, minorization–maximization principles and EM algorithm. A BIC-type criterion will be utilized for the selection of the tuning parameter in the penalized likelihood approaches. The effectiveness of the proposed method is supported by simulation studies and a real-world example.
Kriging (or Gaussian process regression) becomes a popular machine learning method for its flexibility and closed-form prediction expressions. However, one of the key challenges in applying kriging to engineering syst...
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Kriging (or Gaussian process regression) becomes a popular machine learning method for its flexibility and closed-form prediction expressions. However, one of the key challenges in applying kriging to engineering systems is that the available measurement data are scarce due to the measurement limitations or high sensing costs. On the other hand, physical knowledge of the engineering system is often available and represented in the form of partial differential equations (PDEs). We present in this paper a PDE-informed Kriging model (PIK) that introduces PDE information via a set of PDE points and conducts posterior prediction similar to the standard kriging method. The proposed PIK model can incorporate physical knowledge from both linear and nonlinear PDEs. To further improve learning performance, we propose an active PIK framework (APIK) that designs PDE points to leverage the PDE information based on the PIK model and measurement data. The selected PDE points not only explore the whole input space but also exploit the locations where the PDE information is critical in reducing predictive uncertainty. Finally, an expectation-maximization algorithm is developed for parameter estimation. We demonstrate the effectiveness of APIK in two synthetic examples: a shock wave case study and a laser heating case study.
As a technique to investigate behaviours of a computer network with low operational cost, network tomography has received considerable attentions in recent years. Most studies in this area assume that the topology of ...
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As a technique to investigate behaviours of a computer network with low operational cost, network tomography has received considerable attentions in recent years. Most studies in this area assume that the topology of the network of interest is known, and try to propose computationally and/or statistically efficient methods to estimate link-level properties such as loss rate, delay distribution, bandwidth etc., or global traffic properties such as point-to-point traffic matrix. Little progresses have been made for scenarios when topology of the target network is unknown, although it is often the case in many practical applications. The few published works for topology tomography resolved the problem primarily by clustering analysis, which works for tree-like networks only and often suffers from unstable performance for large networks of complicated structure. In this article, we study the classic problem of network tomography from a new perspective. By connecting the problem of topology tomography to the classic machine learning problem of "market basket analysis," we find that simultaneous topology and loss tomography can be achieved by discovering association patterns of loss records collected at receivers, which can be efficiently resolved with light modifications of a recently developed statistical method known as the "theme dictionary model". Both theoretical analysis and simulation studies demonstrate that the proposed approach enjoys improved effectiveness for networks of tree as well as general topology with slightly higher computational costs.
To address the issue of a large placebo effect in certain therapeutic areas, rather than the application of the traditional gold standard parallel group placebo-controlled design, different versions of the sequential ...
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To address the issue of a large placebo effect in certain therapeutic areas, rather than the application of the traditional gold standard parallel group placebo-controlled design, different versions of the sequential parallel comparison design have been advocated. In general, the design consists of two consecutive stages and three treatment groups. Stage 1 placebo nonresponders potentially form a prespecified patient subgroup for formal between-treatment comparison at the final analysis. In this research, a version of the design is considered for a binary endpoint. To fully utilize all available data, a generalized weighted combination test is proposed in case placebo has a relatively small effect for some of the study endpoints. The weighted combination of the test based on stage 1 data and the test based on stage 2 data of stage 1 placebo nonresponders suggested in the literature uses only a part of the study data and is a special case of this generalized weighted combination test. A multiple imputation approach is outlined for handling missing not at random data. Simulation is conducted to evaluate the performances of the methods and a data example is employed to illustrate the applications of the methods.
Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Copulas can capture different types of joint tail characteristics beyond the Gaussian ...
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Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Copulas can capture different types of joint tail characteristics beyond the Gaussian paradigm. Maintaining spatial homogeneity and continuity around the observed random spatial point is also challenging. Especially when interpolating along a spatial surface, the boundary points also demand focus in forming a neighborhood. As a result, importing the concept of hierarchical clustering on the spatial random field is necessary for developing the copula model with the interface of the expectation-maximization algorithm and concurrently utilizing the idea of the Bayesian framework. This article introduces a spatial cluster-based C-vine copula and a modified Gaussian distance kernel to derive a novel spatial probability distribution. To make spatial copula interpolation compatible and efficient, we estimate the parameter by employing different techniques. We apply the proposed spatial interpolation approach to the air pollution of Delhi as a crucial circumstantial study to demonstrate this newly developed novel spatial estimation technique.
In this paper, we consider the identification problem for nonlinear state-space models with skewed measurement noises. The generalized hyperbolic skew Student's t (GHSkewt) distribution is employed to describe the...
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In this paper, we consider the identification problem for nonlinear state-space models with skewed measurement noises. The generalized hyperbolic skew Student's t (GHSkewt) distribution is employed to describe the skewed noises and formulate the hierarchical model of the considered system. A unified framework for estimating unknown states and model parameters is presented based on expectation-maximization (EM) algorithm, in which the forward filtering backward simulation with rejection sampling (RS-FFBSi) is employed to efficiently estimate the smoothing densities of the hidden states, and optimization method is adopted to update model parameters. One numerical study and the electro-mechanical positioning system (EMPS) are employed to verify the effectiveness of the developed approach.
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