Publications (43)
A Probit Network Model with Arbitrary Dependence
Ting Yan
In this paper, we adopt a latent variable method to formulate a network model with arbitrarily dependent structure. We assume that the latent variables follow a multivariate normal…
A two component jet model for the X-ray afterglow flat segment in short GRB 051221A
Zhi-Ping Jin, Ting Yan, Yi-Zhong Fan +1
In the double neutron star merger or neutron star-black hole merger model for short GRBs, the outflow launched might be mildly magnetized and neutron rich. The magnetized neutron-r…
Testing degree heterogeneity in directed networks
Lu Pan, Qiuping Wang, Ting Yan
In this study, we focus on the likelihood ratio tests in the model for testing degree heterogeneity in directed networks, which is an exponential family distribution on direc…
Maximum likelihood estimation in the sparse Rasch model
Pai Peng, Lianqiang Qu, Qiuping Wang +2
The Rasch model has been widely used to analyse item response data in psychometrics and educational assessments. When the number of individuals and items are large, it may be impra…
Grouped sparse paired comparisons in the Bradley-Terry model
Ting Yan, Jinfeng Xu, Yaning Yang
In a wide class of paired comparisons, especially in the sports games, in which all subjects are divided into several groups, the intragroup comparisons are dense and the intergrou…
Likelihood ratio tests in random graph models with increasing dimensions
Ting Yan, Yuanzhang Li, Jinfeng Xu +2
We explore the Wilks phenomena in two random graph models: the -model and the Bradley-Terry model. For two increasing dimensional null hypotheses, including a specified null $H…
L-2 Regularized maximum likelihood for -model in large and sparse networks
Meijia Shao, Yu Zhang, Qiuping Wang +3
The -model is a powerful tool for modeling large and sparse networks driven by degree heterogeneity, where many network models become infeasible due to computational challenge…
Asymptotic theory in network models with covariates and a growing number of node parameters
Qiuping Wang, Yuan Zhang, Ting Yan
We propose a general model that jointly characterizes degree heterogeneity and homophily in weighted, undirected networks. We present a moment estimation method using node degrees…
Approximating the inverse of a diagonally dominant matrix with positive elements
Ting Yan
For an diagonally dominant matrix with positive elements satisfying certain bounding conditions, we propose to use a diagonal matrix $S=(s_{i,…
Optimal estimators and tests for reciprocal effects
Qunqiang Feng, Jiashun Jin, Yaru Tian +1
The model plays a fundamental role in modeling directed networks, where the reciprocal effect parameter is of special interest in practice. However, due to nonlinear fac…
Wilks' theorems in some exponential random graph models
Ting Yan, Yuanzhang Li, Jinfeng Xu +2
We are concerned here with the likelihood ratio statistics in two exponential random graph models -- the -model and the Bradley-Terry model, in which the degree sequence on an…
Directed Networks with a Differentially Private Bi-degree Sequence
Ting Yan
Although a lot of approaches are developed to release network data with a differentially privacy guarantee, inference using noisy data in many network models is still unknown or no…
Differentially private analysis of networks with covariates via a generalized -model
Ting Yan
How to achieve the tradeoff between privacy and utility is one of fundamental problems in private data analysis.In this paper, we give a rigourous differential privacy analysis of…
Corrected Bayesian information criterion for stochastic block models
Jianwei Hu, Hong Qin, Ting Yan +1
Estimating the number of communities is one of the fundamental problems in community detection. We re-examine the Bayesian paradigm for stochastic block models and propose a "corre…
Optical Convolutional Spectrometer
Chunhui Yao, Jie Ma, Ningning Wang +12
Optical spectrometers are fundamental across numerous disciplines in science and technology. However, miniaturized versions, while essential for in situ measurements, are often res…
Time-varying -model for dynamic directed networks
Yuqing Du, Lianqiang Qu, Ting Yan +1
We extend the well-known -model for directed graphs to dynamic network setting, where we observe snapshots of adjacency matrices at different time points. We propose a kernel-s…
Ranking in the generalized Bradley-Terry models when the strong connection condition fails
Ting Yan
For nonbalanced paired comparisons, a wide variety of ranking methods have been proposed. One of the best popular methods is the Bradley-Terry model in which the ranking of a set o…
Using Maximum Entry-Wise Deviation to Test the Goodness-of-Fit for Stochastic Block Models
Jianwei Hu, Jingfei Zhang, Hong Qin +2
The stochastic block model is widely used for detecting community structures in network data. How to test the goodness-of-fit of the model is one of the fundamental problems and ha…
Affiliation networks with an increasing degree sequence
Yong Zhang, Xiaodi Qian, Hong Qin +1
Affiliation network is one kind of two-mode social network with two different sets of nodes (namely, a set of actors and a set of social events) and edges representing the affiliat…
Asymptotic Theory for Differentially Private Generalized -models with Parameters Increasing
Yifan Fan, Huiming Zhang, Ting Yan
Modelling edge weights play a crucial role in the analysis of network data, which reveals the extent of relationships among individuals. Due to the diversity of weight information,…
Chip-scale sensor for spectroscopic metrology
Chunhui Yao, Wanlu Zhang, Peng Bao +10
Miniaturized spectrometers hold great promise for in situ, in vitro, and even in vivo sensing applications. However, their size reduction imposes vital performance constraints in m…
A degree-corrected Cox model for dynamic networks
Yuguo Chen, Lianqiang Qu, Jinfeng Xu +2
Continuous time network data have been successfully modeled by multivariate counting processes, in which the intensity function is characterized by covariate information. However,…
A central limit theorem in the -model for undirected random graphs with a diverging number of vertices
Ting Yan, Jinfeng Xu
Chatterjee, Diaconis and Sly (2011) recently established the consistency of the maximum likelihood estimate in the -model when the number of vertices goes to infinity. By appro…
Inference in semiparametric formation models for directed networks
Lianqiang Qu, Lu Chen, Ting Yan +1
We propose a semiparametric model for dyadic link formations in directed networks. The model contains a set of degree parameters that measure different effects of popularity or out…
Triple-dyad ratio estimation for the model
Qunqiang Feng, Yaru Tian, Ting Yan
Although the model was proposed 40 years ago, little progress has been made to address asymptotic theories in this model, that is, neither consistency of the maximum likeliho…
Inference in a generalized Bradley-Terry model for paired comparisons with covariates and a growing number of subjects
Ting Yan
Motivated by the home-field advantage in sports, we propose a generalized Bradley--Terry model that incorporates covariate information for paired comparisons. It has an -dimensi…
Wilks' theorems in the -model
Ting Yan, Yuanzhang Li, Jinfeng Xu +2
Likelihood ratio tests and the Wilks theorems have been pivotal in statistics but have rarely been explored in network models with an increasing dimension. We are concerned here wi…
The influence of fallback discs on the spectral and timing properties of neutron stars
Ting Yan, Rosalba Perna, Roberto Soria
Fallback discs around neutron stars (NSs) are believed to be an expected outcome of supernova explosions. Here we investigate the consequences of such a common outcome for the timi…
Subgraph counting estimation for the -model in sparse networks
Qunqiang Feng, Jiashun Jin, Yaru Tian +1
The -model is popular for characterizing the commonly observed degree heterogeneity phenomenon in real-world networks. In this study, we develop a cycle counting approach to es…
Asymmetrical estimator for training encapsulated deep photonic neural networks
Yizhi Wang, Minjia Chen, Chunhui Yao +4
Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency and lower cost. Howe…
3D surface profiling via photonic integrated geometric sensor
Ziyao Zhang, Yizhi Wang, Chunhui Yao +11
Measurements of microscale surface patterns are essential for process and quality control in industries across semiconductors, micro-machining, and biomedicines. However, the devel…
Asymptotic generalized bivariate extreme with random index
M. A. Abd Elgawad, A. M. Elsawah, Hong Qin +1
In many biological, agricultural, military activity problems and in some quality control problems, it is almost impossible to have a fixed sample size, because some observations ar…
Moment estimation in paired comparison models with a growing number of subjects
Qiuping Wang, Lu Pan, Ting Yan
When the number of subjects, , is large, paired comparisons are often sparse. Here, we study statistical inference in a class of paired comparison models parameterized by a set…
Statistical Inference in a Directed Network Model with Covariates
Ting Yan, Binyan Jiang, Stephen E. Fienberg +1
Networks are often characterized by node heterogeneity for which nodes exhibit different degrees of interaction and link homophily for which nodes sharing common features tend to a…
A two-way heterogeneity model for dynamic networks
Binyan Jiang, Chenlei Leng, Ting Yan +2
Dynamic network data analysis requires joint modelling individual snapshots and time dynamics. This paper proposes a new two-way heterogeneity model towards this goal. The new mode…
Invisible Active Galactic Nuclei. II Radio Morphologies & Five New HI 21 cm Absorption Line Detections
Ting Yan, John T. Stocke, Jeremy Darling +3
We have selected a sample of 80 candidates for obscured radio-loud active galactic nuclei and presented their basic optical/near-infrared (NIR) properties in Paper 1. In this paper…
Inference in the model for directed networks under local differential privacy
Xueying Sun, Ting Yan, Binyan Jiang
We explore the edge-flipping mechanism, a type of input perturbation, to release the directed graph under edge-local differential privacy. By using the noisy bi-degree sequence fro…
A Unified Framework for Inference in Network Models with Degree Heterogeneity and Homophily
Ting Yan
The degree heterogeneity and homophily are two typical features in network data. In this paper, we formulate a general model for undirected networks with these two features and pre…
Asymptotic normality in the maximum entropy models on graphs with an increasing number of parameters
Ting Yan, Yunpeng Zhao, Hong Qin
Maximum entropy models, motivated by applications in neuron science, are natural generalizations of the -model to weighted graphs. Similar to the -model, each vertex in max…
Asymptotics in directed exponential random graph models with an increasing bi-degree sequence
Ting Yan, Chenlei Leng, Ji Zhu
Although asymptotic analyses of undirected network models based on degree sequences have started to appear in recent literature, it remains an open problem to study statistical pro…
Temporal network analysis via a degree-corrected Cox model
Yuguo Chen, Lianqiang Qu, Jinfeng Xu +2
Temporal dynamics, characterised by time-varying degree heterogeneity and homophily effects, are often exhibited in many real-world networks. As observed in an MIT Social Evolution…
Approximating the inverse of a balanced symmetric matrix with positive elements
Ting Yan, Xu Jinfeng
For an balanced symmetric matrix with positive elements satisfying and certain bounding conditions, we propose to use th…
Semiparametric analysis for paired comparisons with covariates
Haoyue Song, Lianqiang Qu, Ting Yan +1
Statistical inference in parametric models (e.g., the Bradley--Terry model and its variants) for paired-comparison data has been explored in the high-dimensional regime, in which t…