Inferring network structures remains an interesting question for its importance on the understanding and controlling collective dynamics of complex systems. The existing shrinking methods such as Lasso-type estimation can not suitably reveal such property. A new method recently suggested, called by signal lasso (or its updating version: adaptive signal lasso) was proposed to solve the network reconstruction problem, where the signal parameter can be shrunk to either 0 or 1 in two different directions. The signal lasso or adaptive signal lasso employed the additive penalty of signal and non-signal terms which is a convex function and easily to complementation in computation. However their methods need tuning the one or two parameters to find an optimal solution, which is time cost for large size network. In this paper we propose new signal lasso method based on two penalty functions to estimate the signal parameter and uncovering network topology in complex network with a small amount of observations. The penalty functions we introduced are non-convex function, thus coordinate descent algorithms are suggested. We find in this method the tuning parameter can be set to a large enough values such that the signal parameter can be completely shrunk either 0 or 1. The extensive simulations are conducted in linear regression models with different assumptions, the evolutionary-game-based dynamic model and Kuramoto model of synchronization problem. The advantage and disadvantage of each method are fully discussed in various conditions. Finally a real example comes from behavioral experiment is used for illustration. Our results show that signal lasso with non-convex penalties is effective and fast in estimating signal parameters in linear regression model.

This paper establishes identifiability results for mixture regression models with skew-normal errors under both fixed and random designs. We propose a novel penalized maximum likelihood estimation method for such models and demonstrate the strong consistency of the proposed estimator. An EM-type algorithm is developed to derive the penalized estimator. The finite sample properties of the proposed methodology are examined using extensive simulations, and a real data example is presented for illustration.

This paper considers the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients. B-spline is employed for the approximation of varying coefficients. The instrumental variable quantile regression approach is employed for parameter estimation. The rank score tests are developed for hypotheses on the coefficients, including the hypotheses on the non-varying coefficients and the constancy of the varying coefficients. The asymptotic properties of the proposed estimators and test statistics are both established. Monte Carlo simulations are conducted to study the finite sample performance of the proposed method. Analysis of a real data example is presented for illustration.

In complex social systems, individual relationships and the surrounding environment are constantly changing, allowing individuals to interact on dynamic networks. This study aims to investigate how individuals in a dynamic network engaged in a prisoner’s dilemma game adapt their competitive environment through random edge breaks and reconnections when faced with incomplete information and adverse local conditions, thereby influencing the evolution of cooperative behavior. We find that random edge breaks and reconnections in dynamic networks can disrupt cooperative clusters, significantly hindering the development of cooperation. This negative impact becomes more pronounced over larger time scales. However, we also observe that nodes with higher degrees of connectivity exhibit greater resilience to this cooperation disruption. Our research reveals the profound impact of dynamic network structures on the evolution of cooperation and provides new insights into the mechanisms of cooperation in complex systems.

High-dimensional parameter testing is commonly used in bioinformatics to analyze complex relationships in gene expression and brain connectivity studies, involving parameters like means, covariances, and correlations. In this paper, we present a novel approach for testing U-statistics-type parameters by leveraging jackknife pseudo-values. Inspired by Tukey’s conjecture, we establish the asymptotic independence of these pseudo-values, allowing us to reformulate U-statistics-type parameter testing as a sample mean testing problem. This reformulation enables the use of established sample mean testing frameworks, simplifying the testing procedure. We apply a multiplier bootstrap method to obtain critical values and provide a rigorous theoretical analysis to validate the approach. Simulation studies demonstrate the robustness of our method across a variety of scenarios. Additionally, we apply our approach to investigate differences in the dependency structures of a subset of genes within the Wnt signaling pathway, which is associated with lung cancer.

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure.

Network reconstruction is a crucial task in understanding and controlling the collective dynamics of complex systems. Most real-world networks exhibit sparse properties, and the connection parameter is a binary signal (0 or 1). Traditional shrinkage methods, such as lasso or compressed sensing (CS), are not suitable for revealing this property. Recently, the signal lasso method was introduced to solve the network reconstruction problem, which was found to be more effective than lasso and CS methods. However, the signal lasso method has a limitation: it cannot accurately classify estimated coefficients that fall between 0 and 1. To address this issue, this paper proposes a method called adaptive signal lasso, which can accurately estimate the signal parameter and uncover the network topology in complex networks with a small number of observations. Our proposed method has at least three advantages: first, it is highly effective in uncovering the network topology and can completely shrink the signal parameter to either 0 or 1, eliminating the unclassified portion in network reconstruction; second, it performs well in both sparse and nonsparse signal scenarios and is robust to noise contamination; third, it only requires the selection of one tuning parameter, reducing computational cost and making it easy to apply. Theoretical properties of this method have been studied, and numerical simulations from linear regression, evolutionary game, and the Kuramoto model are deeply explored. Finally, two real-world examples from human behavioral experiments and the world trade web are used for illustration. It is expected that our proposed method will establish a reliable and uniform framework for estimating signal parameters in complex systems.

For a finite mixture of skew normal distributions, the maximum likelihood estimator is not well-defined because of the unboundedness of the likelihood function when scale parameters go to zero and the divergency of the skewness parameter estimates. To overcome these two problems simultaneously, we propose constrained maximum likelihood estimators under constraints on both the scale parameters and the skewness parameters. The proposed estimators are consistent and asymptotically efficient under relaxed constraints on the scale and skewness parameters. Numerical simulations show that in finite sample cases the proposed estimators outperform the ordinary maximum likelihood estimators. Two real datasets are used to illustrate the success of the proposed approach.

In human society, individual interactions intrinsically change, with profound implications for epidemics. The activity-driven model, a type of temporal network, offers an excellent framework to study epidemic processes in dynamical interaction. In this work, we study how social attitudes affect the transmission of infectious diseases in activity-driven net-works. Here, we divide a population into "risk-ignorant" and "risk-averse", in which risk -averse individuals will reduce their social intensity (Social intensity refers to the number of social contacts in the social process) and risk-ignorant individuals will not. A parameter p controls the proportion of risk-averse individuals, and therefore risk-ignorant individu-als by 1-p. With the aid of mean-field theory, we calculate epidemic thresholds, as well as validate theoretical predictions with extensive Monte Carlo simulations. It is shown nu-merically and theoretically that reducing the social intensity and increasing the number of risk-averse individuals are effective ways of controlling epidemic outbreaks. An appropriate proportion of the risk-averse individual will lead to an epidemic die-out, which is based on a small spreading rate. Our research provides a new perspective for understanding the effect of the population with different social attitudes in the epidemic process.(c) 2023 Elsevier Inc. All rights reserved.

In meta-analysis model, due to the appearance of publication bias or outliers, as well as the small sample size, the normal assumption is usually unreliable. Therefore, the exploration of more robust estimation, such quantile regression (QR) method, is extremely important in meta-analysis area. This paper studies the QR estimation method in random-effects meta-analysis model based on the reformulation by asymmetric Laplace distribution (ALD). The maximum likelihood estimation using Monte Carlo Expectation Maximization algorithm and the Bayesian estimation using Markov chain Monte Carlo (MCMC) algorithm are proposed for computation of the QR estimates. The significance tests of regression coefficients are suggested using likelihood ratio statistics. For MCMC algorithm, a simple and efficient Gibbs sampling algorithm is employed based on a location-scale mixture representation of the ALD, and information criterions are considered for choosing the hyper-parameters. Monte Carlo simulations are conducted to study the finite sample performance of the proposed methodology and analysis of two real data sets are presented for illustrations. Our results show that QR estimation methods perform very well, especially in case of non-normal assumption in meta-regression models. The detailed algorithms and software code are available for easy use in applications.

Lei Shi1,2,∗ Chen Shen, Libin Jin, Qi Shi, Zhen Wang, Marko Jusup, and Stefano Boccaletti 1. School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, China. 2. Interdisciplinary Research Institute of Data Science, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China. 3. Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an 710072, China. 4. CNR Institute for Complex Systems, Via Madonna del Piano 10, 50019 Florence, Italy. 5. Unmanned Systems Research Institute, Northwestern Polytechnical University, Xi’an, 710072, China. 6. Tokyo Tech World Hub Research Initiative (WRHI), Institute for Innovative Research, Tokyo Institute of Technology, 152-8550 Tokyo, Japan. 7. Moscow Institute of Physics and Technology, Institutskiy per., Dolgoprudny, Moscow Region, 141701 Russia. (Dated: April 7, 2021)

Inferring the connectivity structure of networked systems from data is an extremely important task in many areas of science. Most of real-world networks exhibit sparsely connected topologies, with links between nodes that in some cases may be even associated to a binary state (0 or 1, denoting respectively the absence or the existence of a connection). Such un-weighted topologies are elusive to classical reconstruction methods such as Lasso or Compressed Sensing techniques. We here introduce a novel approach called signal Lasso, where the estimation of the signal parameter is subjected to 0 or 1 values. The theoretical properties and algorithm of proposed method are studied in detail. Applications of the method are illustrated to an evolutionary game and synchronization dynamics in several synthetic and empirical networks, where we show that the novel strategy is reliable and robust, and outperform the classical approaches in terms of accuracy and mean square errors.

This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.

The locally weighted censored quantile regression approach is proposed for panel data models with fixed effects, which allows for random censoring. The resulting estimators are obtained by employing the fixed effects quantile regression method. The weights are selected either parametrically, semi-parametrically or non-parametrically. The large panel data asymptotics are used in an attempt to cope with the incidental parameter problem. The consistency and limiting distribution of the proposed estimator are also derived. The finite sample performance of the proposed estimators are examined via Monte Carlo simulations.

Skew normal mixture models provide a more flexible framework than the popular normal mixtures for modelling heterogeneous data with asymmetric behaviors. Due to the unboundedness of likelihood function and the divergency of shape parameters, the maximum likelihood estimators of the parameters of interest are often not well defined, leading to dissatisfactory inferential process. We put forward a proposal to deal with these issues simultaneously in the context of penalizing the likelihood function. The resulting penalized maximum likelihood estimator is proved to be strongly consistent when the putative order of mixture is equal to or larger than the true one. We also provide penalized EM-type algorithms to compute penalized estimators. Finite sample performances are examined by simulations and real data applications and the comparison to the existing methods.

This paper studies Bayesian local influence analysis for the spatial autoregressive models with heteroscedasticity (heteroscedastic SAR models). Two local diagnostic procedures using curvature-based and slope-based methods are proposed in the framework of Bayesian perspective. The curvature-based diagnostic are obtained by maximizing the normal curvature of an influence graph based on Kullback–Leibler divergence measure and slope-based diagnostic use the first order derivative of Bayesian factor defined for perturbation. Three perturbation schemes under the heteroscedastic SAR models are suggested and the diagnostic measures are derived respectively. The computations for the proposed diagnostic measures can be easily obtained using Markov Chain Monte Carlo sampler. The proposed methodologies are illustrated using two real examples.

本文针对广义空间模型中出现的异常值提出了相应的修正模型.分别基于广义空间模型的均值扰动和方差扰动下提出了对应的异常值修正模型,并给出了参数估计方法.通过将该方法应用于中国能源利用效率的区域特征问题,分析了其中的异常值并建立了异常值修正模型.该修正模型能有效改进模型拟合效果,为处理数据中出现的异常值提供了一种新的思路.

The free-rider behavior is widespread in the system, which will not only lead to social dilemma, and even make the entire system collapse. In order to overcome this complex problem, scientists have done a strenuous endeavor. The public goods game is significant for the study of cooperative behavior among complex interactive social system. And much attention has been paid to the proposal of reward and punishment system. Although people always want to reward cooperative behavior in many cases, antisocial behavior is also common in complex human and biological communities, and free-riders may be rewarded, especially without their information. Therefore, we study the public good game with neutral reward in order to explore the evolution of cooperation. In our public good game, individuals with a few strategies reward other individuals with most strategies in a same group, and the dominant players will receive a fixed bonus provide by other vulnerable players. We show that increasing the bonus will directly promote cooperation and resolve the social dilemma.

This article studies the outlier detection problem in mixed regressive-spatial autoregressive model. The formulae for testing outliers and their approximate distributions are derived under the mean-shift model and the variance-weight model, respectively. The simulation studies are conducted for examining the power and size of the test, as well as for the detection of outliers when a simulated data contains several outliers. A real data is analyzed to illustrate the proposed method, and modified models based on mean-shift and variance-weight models in which detected outliers are taken into account are suggested to deal with the outliers and confirm theconclusions.

We study the local influence in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. The stepwise local influence procedure is employed in our diagnostic analysis. We derive the local diagnostic measures in the general spatial model under three perturbation schemes, namely, the variance perturbation, dependent variable perturbation and explanatory variable perturbation schemes. A simulation example and two real-data examples are analysed in detail and they show that the stepwise local influence analysis is effective in identifying influential observations and is a powerful tool for uncovering masking effects.