Recommending Friends Instantly in Location-based Mobile Social Networks

Differently from the general online social network(OSN),locationbased mobile social network(LMSN),which seamlessly integrates mobile computing and social computing technologies,has unique characteristics of temporal,spatial and social correlation.Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN.However,the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world.In this article,we analyze users' check-in behavior in a real LMSN site named Gowalla.According to this analysis,we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity,offline behavior similarity and friendship network information in the virtual community simultaneously.This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community.Finally,we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.

Optimal Timing of Business Conversion for Solvency Improvement

In this paper,we study the optimal timing to convert the risk of business for an insurance company in order to improve its solvency.The cash flow of company evolves according to a jump-diffusion process.Business conversion option offers the company an opportunity to transfer the jump risk business out.In exchange for this option,the company needs to pay both fixed and proportional transaction costs.The proportional cost can also be seen as the profit loading of the jump risk business.We formulated this problem as an optimal stopping problem.By solving this stopping problem,we find that the optimal timing of business conversion mainly depends on the profit loading of the jump risk business.A larger profit loading would make the conversion option valueless.The fixed cost,however,only delays the optimal timing of business conversion.In the end,numerical results are provided to illustrate the impacts of transaction costs and environmental parameters to the optimal strategies.

Modifiable risk factors for esophageal cancer in endoscopic screening population:A modeling study

To the Editor:Esophageal cancer,one of the most common cancer types in China,with an estimated 346,633 new cases and 323,600 deaths in 2022,is becoming an increasingly serious clinical and public health problem.^([1])The successful promotion of the self-management strategy has indicated that lifestyle modifications can be valuable in the primary prevention of cancer development.Adopting a healthy lifestyle has become a novel strategy for primary prevention and risk reduction in high-risk areas.Previous epidemiological studies have identified several lifestyle-related risk factors for esophageal cancer,including smoking and diet.^([2])Each factor can typically explain a modest proportion of cancer risk.However,when combined,these known risk factors may substantially affect the risk of esophageal cancer.Nevertheless,some risk factors for esophageal cancer are non-modifiable,including age,low socioeconomic status,and family history.Whether and how these non-modifiable risk factors affect primary cancer prevention by intervening with modifiable risk factors remain unclear.

Acceleration of the EM Algorithm Using the Vector Aitken Method and Its Steffensen Form

Based on Vector Aitken （VA） method, we propose an acceleration Expectation-Maximization （EM） algorithm, VA-accelerated EM algorithm, whose convergence speed is faster than that of EM algorithm. The VA-accelerated EM algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the EM algorithm, thus it keeps the flexibility and simplicity of the EM algorithm. Considering Steffensen iterative process, we have also given the Steffensen form of the VA-accelerated EM algorithm. It can be proved that the reform process is quadratic convergence. Numerical analysis illustrate the proposed methods are efficient and faster than EM algorithm.

Empirical likelihood-based dimension reduction inference for linear error-in-responses models with validation study

In this paper, linear errors-in-response models are considered in the presence of validation data on the responses. A semiparametric dimension reduction technique is employed to define an estimator of β with asymptotic normality, the estimated empirical loglikelihoods and the adjusted empirical loglikelihoods for the vector of regression coefficients and linear combinations of the regression coefficients, respectively. The estimated empirical log-likelihoods are shown to be asymptotically distributed as weighted sums of independent X 2 1 and the adjusted empirical loglikelihoods are proved to be asymptotically distributed as standard chi-squares, respectively.

Raising the retirement age:the impact on the individual and actuarial balance for Chinese urban workers’basic pensions

Rapid population ageing and increasing longevity are raising concerns about the sustainability of the basic pension systems in China.Raising the retirement age,as an important way to maintain long-term financial sustainability,has become the main policy choice for China.Some studies show that postponing retirement can solve the financial pressures of pension systems effectively.However,if the pension benefits increase with the pensionable age,this may offset some effects and even have a negative impact on the financial balance.This paper builds cohort models and period actuarial balance models for Chinese urban workers’basic pension system to measure the cohort and period effects of postponing retirement,with the aim of analysing the change in the individual pension net wealth and the long-term actuarial balance of the system with population ageing and increasing life expectancy.The result shows that raising the retirement age,which is linked to life expectancy,will lead to an increase in the pension benefits,individual net pension wealth and then pension fund expenditure.It may benefit the individual and short-term actuarial balance but have a small effect on the long-term actuarial balance of the system.

A combined p-value test for the mean difference of high-dimensional data

This paper proposes a novel method for testing the equality of high-dimensional means using a multiple hypothesis test. The proposed method is based on the maximum of standardized partial sums of logarithmic p-values statistic. Numerical studies show that the method performs well for both normal and non-normal data and has a good power performance under both dense and sparse alternative hypotheses. For illustration, a real data analysis is implemented.

Regression Analysis of Interval-Censored Data with Informative Observation Times Under the Accelerated Failure Time Model

This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.

Symmetrical Independence Tests for Two Random Vectors with Arbitrary Dimensional Graphs

Test of independence between random vectors X and Y is an essential task in statistical inference.One type of testing methods is based on the minimal spanning tree of variables X and Y.The main idea is to generate the minimal spanning tree for one random vector X,and for each edges in minimal spanning tree,the corresponding rank number can be calculated based on another random vector Y.The resulting test statistics are constructed by these rank numbers.However,the existed statistics are not symmetrical tests about the random vectors X and Y such that the power performance from minimal spanning tree of X is not the same as that from minimal spanning tree of Y.In addition,the conclusion from minimal spanning tree of X might conflict with that from minimal spanning tree of Y.In order to solve these problems,we propose several symmetrical independence tests for X and Y.The exact distributions of test statistics are investigated when the sample size is small.Also,we study the asymptotic properties of the statistics.A permutation method is introduced for getting critical values of the statistics.Compared with the existing methods,our proposed methods are more efficient demonstrated by numerical analysis.

Regression Analysis of Misclassified Current Status Data with Informative Observation Times

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation times.It is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading results.In this paper,the authors consider such data and propose a frailty-based inference procedure.In particular,an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established.The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.

Momentum Effect Differs Across Stock Performances:Chinese Evidence

Prior empirical studies find positive and negative momentum effect across the global nations, but few focus on explaining the mixed results. In order to address this issue, we apply the quantile regression approach to analyze the momentum effect in the context of Chinese stock market in this paper. The evidence suggests that the momentum effect in Chinese stock is not stable across firms with different levels of performance. We find that negative momentum effect in the short and medium horizon （3 months and 9 months） increases with the quantile of stock returns. And the positive momentum effect is observed in the long horizon （12 months）, which also intensifies for the high performing stocks. According to our study, momentum effect needs to be examined on the basis of stock returns. OLS estimation, which gives an exclusive and biased result, provides misguiding intuitions for momentum effect across the global nations. Based on the empirical results of quantile regression, effective risk control strategies can also be inspired by adjusting the proportion of assets with past performances.

The large sample property of the iterative generalized least squares estimation for hierarchical mixed effects model

In many fields, we need to deal with hierarchically structured data.For this kind of data, hierarchical mixed effects model can show the correlationof variables in the same level by establishing a model for regression coefficients.Due to the complexity of the random part in this model, seeking an effectivemethod to estimate the covariance matrix is an appealing issue. Iterative generalizedleast squares estimation method was proposed by Goldstein in 1986 and wasapplied in special case of hierarchical model. In this paper, we extend themethod to the general hierarchical mixed effects model, derive its expressions indetail and apply it to economic examples.

A New Class of Strong Orthogonal Arrays of Strength Three

Strong orthogonal arrays(SOAs) were recently introduced and studied as a class of spacefilling designs for computer experiments. To surely realize better space-filling properties, SOAs of strength three or higher are desirable. In addition, orthogonality is also an important property for designs of computer experiments, because it guarantees that the estimates of the main effects are uncorrelated. This paper first provides a systematic study on the construction of(nearly) orthogonal strength-three SOAs with better space-filling properties. The newly proposed strength-three SOAs enjoy almost the same space-filling properties of strength-four SOAs, and can accommodate much more columns than the latter. Moreover, they are(nearly) orthogonal and flexible in run sizes. The construction methods are straightforward to implement, and their theoretical supports are well established. In addition to the theoretical results, many designs are tabulated for practical needs.

First Passage Density of Brownian Motion with Two-sided Piecewise Linear Boundaries

The first passage time has many applications in fields like finance,econometrics,statistics,and biology.However,explicit formulas for the first passage density have only been obtained for a few cases.This paper derives an explicit formula for the first passage density of Brownian motion with twosided piecewise continuous boundaries which may have some points of discontinuity.Approximations are used to obtain a simplified formula for estimating the first passage density.Moreover,the results are also generalized to the case of two-sided general nonlinear boundaries.Simulations can be easily carried out with Monte Carlo method and it is demonstrated for several typical two-sided boundaries that the proposed approximation method offers a highly accurate approximation of first passage density.

核实数据帮助下误差在反映线性模型经验似然降维推断

在核实数据帮助下,考虑误差在反映线性模型.半参数降维技术分别应用于定义β的渐近正态估计和β与其线性组合的被估计经验似然及调整经验似然.我们分别证明被估计的经验对数似然及其调整的经验对数似然渐近于独立卡方变量加权和的分布及标准卡方分布.

A penalized integrative deep neural network for variable selection among multiple omics datasets

Deep learning has been increasingly popular in omics data analysis.Recent works incorporating variable selection into deep learning have greatly enhanced the model’s interpretability.However,because deep learning desires a large sample size,the existing methods may result in uncertain findings when the dataset has a small sample size,commonly seen in omics data analysis.With the explosion and availability of omics data from multiple populations/studies,the existing methods naively pool them into one dataset to enhance the sample size while ignoring that variable structures can differ across datasets,which might lead to inaccurate variable selection results.We propose a penalized integrative deep neural network(PIN)to simultaneously select important variables from multiple datasets.PIN directly aggregates multiple datasets as input and considers both homogeneity and heterogeneity situations among multiple datasets in an integrative analysis framework.Results from extensive simulation studies and applications of PIN to gene expression datasets from elders with different cognitive statuses or ovarian cancer patients at different stages demonstrate that PIN outperforms existing methods with considerably improved performance among multiple datasets.The source code is freely available on Github(rucliyang/PINFunc).We speculate that the proposed PIN method will promote the identification of disease-related important variables based on multiple studies/datasets from diverse origins.

Semiparametric Quantile Modelling of Hierarchical Data

The classic hierarchical linear model formulation provides a considerable flexibility for modelling the random effects structure and a powerful tool for analyzing nested data that arise in various areas such as biology, economics and education. However, it assumes the within-group errors to be independently and identically distributed （i.i.d.） and models at all levels to be linear. Most importantly, traditional hierarchical models （just like other ordinary mean regression methods） cannot characterize the entire conditional distribution of a dependent variable given a set of covariates and fail to yield robust estimators. In this article, we relax the aforementioned and normality assumptions, and develop a so-called Hierarchical Semiparametric Quantile Regression Models in which the within-group errors could be heteroscedastic and models at some levels are allowed to be nonparametric. We present the ideas with a 2-level model. The level-1 model is specified as a nonparametric model whereas level-2 model is set as a parametric model. Under the proposed semiparametric setting the vector of partial derivatives of the nonparametric function in level-1 becomes the response variable vector in level 2. The proposed method allows us to model the fixed effects in the innermost level （i.e., level 2） as a function of the covariates instead of a constant effect. We outline some mild regularity conditions required for convergence and asymptotic normality for our estimators. We illustrate our methodology with a real hierarchical data set from a laboratory study and some simulation studies.

Robust estimation in inverse problems via quantile coupling

In this article we consider a sequence of hierarchical space model of inverse problems.The underlying function is estimated from indirect observations over a variety of error distributions including those that are heavy-tailed and may not even possess variances or means.The main contribution of this paper is that we establish some oracle inequalities for the inverse problems by using quantile coupling technique that gives a tight bound for the quantile coupling between an arbitrary sample p-quantile and a normal variable,and an automatic selection principle for the nonrandom filters.This leads to the data-driven choice of weights.We also give an algorithm for its implementation.The quantile coupling inequality developed in this paper is of independent interest,because it includes the median coupling inequality in literature as a special case.

Optimal Investment and Excess of Loss Reinsurance with Short-selling Constraint

This paper considers the optimal control problem with constraints for an insurance company. The risk process is assumed to be a jump-diffusion process and the risk can be reduced through an excess of loss （XL） reinsurance. In addition, the surplus can be invested in the financial market. In the financial market, the short-selling constraint is one of the main factors which make models more realistic. Our goal is to find the optimal investment-reinsurance policy without short-selling, which maximizes the expected exponential utility of the terminal wealth. By solving the corresponding Hamilton-Jacobi-Bellman equation, the value function and the optimal investment-reinsurance policy are given in a closed form.

A Longitudinal Study of the Effects of Family Background Factors on Mathematics Achievements Using Quantile Regression

Quantile regression is gradually emerging as a powerful tool for estimating models of conditional quantile functions, and therefore research in this area has vastly increased in the past two decades. This paper, with the quantile regression technique, is the first comprehensive longitudinal study on mathematics participation data collected in Alberta, Canada. The major advantage of longitudinal study is its capability to separate the so-called cohort and age effects in the context of population studies. One aim of this paper is to study whether the family background factors alter performance on the mathematical achievement of the strongest students in the same way as that of weaker students based on the large longitudinal sample of 2000, 2001 and 2002 mathematics participation longitudinal data set. The interesting findings suggest that there may be differential family background factor effects at different points in the mathematical achievement conditional distribution.