MCNet: Multiscale Clustering Network for Two-View Geometry Learning and Feature Matching

Dear Editor, The main components of multi-view geometry and computer vision are robust pose estimation and feature matching. This letter discusses how to recover two-view geometry and match features between a pair of images, and presents MCNet(a multiscale clustering network) as an algorithm for extracting multiscale features. It can identify the true inliers from the established putative correspondences, where outliers may degenerate the geometry estimation. In particular, the proposed MCNet is based on graph clustering.

Incremental age-related one-year MACCE after acute myocardial infarction in the drug-eluting stent era （from KAMIR-NIH registry）

ObjectivesTo 评估年龄相关的一个年专业在在尖锐心肌的梗塞(AMI ) 的经皮的冠的干预(一种总线标准) 以后的不利 cardiocerebrovascular 事件(MACCE ) 。我们从在 2011 年 11 月之间的健康(KAMIR-NIH ) 的朝鲜尖锐心肌的梗塞登记公民 Institue 在 13,104 个 AMI 病人全部的 AMI.MethodsA 以后分析了在年龄和一个年 MACCE 之间的协会， 2015 年 12 月根据年龄被分类进四个组(组我， < 60 年， n = 4199；组 II， 60-70 年， n = 2577；组织 III；70-80 年， n = 2774；组 IV， 80 年， n = 1018 ) 。病人们为 MACCE 合成的一个年被分析(心脏的死亡，心肌的梗塞，目标容器 revascularization，脑血管的事件) 在 AMI.ResultsThe 一个年以后，在 AMI 的 MACCE 是 3.5%( 组我) ， 6.3%( 组 II ) ， 9.6%( 组 III ) 并且 17.6%( 组 IV ) 。在为使参数惊讶的调整以后，分析结果证明有 AMI 的病人有一个年 MACCE 的增长风险[组 II，调整危险比率(aHR )= 1.224， 95% CI：0.965-1.525， P = 0.096；组 III， aHR = 1.316， 95% CI：1.037-1.671， P = 0.024；组 IV， aHR = 1.975， 95% CI：1.500-62.601， P < 0.001 ) 与相比组我。特别，心脏的死亡在玩的主要结束点合成在这效果的一个主要角色(组 II， aHR = 1.335， 95% CI：0.941-1.895， P = 0.106；组 III， aHR = 1.575， 95% CI：1.122-2.210， P = 0.009；组 IV， aHR = 2.803， 95% CI：1.937-4.054， P < 0.001 ).ConclusionsDespite 在 AMI 为一种总线标准推进了技术和药，年龄仍然在临床的结果施加强大的影响。小心的途径，甚至在发达心病学的现代时代被需要为在 AMI 干预年老人口。

The Role of Japanese Candlestick in DVAR Model

The decomposition-based vector autoregressive model(DVAR) provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relationships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and empirical studies. The main finding of this paper is that both lower and upper shadows in Japanese Candlestick Granger contribute to the DVAR model explanation power, and thus, providing useful information for improving the DVAR forecasts. This finding makes sense as it means that the information contained in the lower and upper shadows should be used when modeling the stock returns with DVAR. Empirical studies performed on China SSEC stock index demonstrate that DVAR model with upper and lower shadows as exogenous variables does have informative and valuable out-of-sample forecasts.

Limit theorems for supremum of Gaussian processes over a random interval

Let {X(t), t ≥ 0} be a centered stationary Gaussian process with correlation r(t)such that 1-r(t) is asymptotic to a regularly varying function. With T being a nonnegative random variable and independent of X(t), the exact asymptotics of P(sup_(t∈[0,T])X(t) > x) is considered, as x →∞.

Development Imbalances in China:An Integrated Analysis with the Tsinghua China Balanced Development Index

The principal contradiction facing the Chinese society has evolved to be that between imbalanced and inadequate development and the people’s ever-growing needs for a better life.Given China’s vision for achieving moderate prosperity,it is relevant to conduct theoretical and empirical studies on the nation’s development imbalances.As a quantitative index,the Tsinghua China Balanced Development Index measures the extent to which development is uneven and insuf ficient across regions,re flecting the progress and shortfalls in China’s efforts to promote balanced development.Our findings provide implications for how policymakers may help people’s expectations for a better life materialize by spurring balanced economic,social,environmental and livelihood development across regions.

Statistical inference for the nonparametric and semiparametric hidden Markov model via the composite likelihood approach

In this paper, we propose a new estimation method for a nonparametric hidden Markov model(HMM), in which both the emission model and the transition matrix are nonparametric, and a semiparametric HMM, in which the transition matrix is parametric while emission models are nonparametric. The estimation is based on a novel composite likelihood method, where the pairs of consecutive observations are treated as independent bivariate random variables. Therefore, the model is transformed into a mixture model, and a modified expectation-maximization(EM) algorithm is developed to compute the maximum composite likelihood.We systematically study asymptotic properties for both the nonparametric HMM and the semiparametric HMM. We also propose a generalized likelihood ratio test to choose between the nonparametric HMM and the semiparametric HMM. We derive the asymptotic distribution and prove the Wilk’s phenomenon of the proposed test statistics. Simulation studies and an application in volatility clustering analysis of the volatility index in the Chicago Board Options Exchange(CBOE) are conducted to demonstrate the effectiveness of the proposed methods.

Statistical Inference of Partially Specified Spatial Autoregressive Model

This paper studies estimation of a partially specified spatial autoregressive model with heteroskedasticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, the unknown parameter is estimated by applying the instrumental variable estimation. Under certain sufficient conditions, the proposed estimator for the finite dimensional parameters is shown to be root-n consistent and asymptotically normally distributed; The proposed estimator for the unknown function is shown to be consistent and asymptotically distributed as well, though at a rate slower than root-n. Consistent estimators for the asymptotic variance-covariance matrices of both estimators are provided. Monte Carlo simulations suggest that the proposed procedure has some practical value.

Statistical Inference on Seemingly Unrelated Single-Index Regression Models

In this article,we consider a class of seemingly unrelated single-index regression models.By taking the contemporaneous correlation among equations into account we construct the weighted estimators(WEs)for unknown parameters of the coefficients and the improved local polynomial estimators for the unknown functions,respectively.We establish the asymptotic normalities of these estimators,and show both of them are more asymptotically efficient than those ignoring the contemporaneous correlation.The performances of the proposed procedures are evaluated through simulation studies.

Statistical inference for multivariate longitudinal data with irregular auto-correlated error process

Multivariate longitudinal data arise frequently in a variety of applications,where multiple outcomes are measured repeatedly from the same subject.In this paper,we first propose a two-stage weighted least square estimation procedure for the regression coefficients when the random error follows an irregular autoregressive(AR)process,and establish asymptotic normality properties for the resulting estimators.We then apply the smoothly clipped absolute deviation(SCAD)variable selection approach to determine the order of the AR error process.We further propose a test statistic to check whether multiple responses are correlated at the same observation time,and derive the asymptotic distribution of the proposed test statistic.Several simulated examples and real data analysis are presented to illustrate the finite-sample performance of the proposed method.

A novel nonparametric mixture model for the detection pattern of COVID-19 on Diamond Princess cruise

The outbreak of COVID-19 on the Diamond Princess cruise ship has attracted much attention.Motivated by the PCR testing data on the Diamond Princess,we propose a novel cure mixture nonparametric model to investigate the detection pattern.It combines a logistic regression for the probability of susceptible subjects with a nonparametric distribution for the detection of infected individuals.Maximum likelihood estimators are proposed.The resulting estimators are shown to be consistent and asymptotically normal.Simulation studies demonstrate that the proposed approach is appropriate for practical use.Finally,we apply the proposed method to PCR testing data on the Diamond Princess to show its practical utility.

Quantile residual lifetime for left-truncated and right-censored data

This article proposes a simple nonparametric estimator of quantile residual lifetime function under left-truncated and right-censored data. The asymptotic consistency and normality of this estimator are proved and the variance expression is calculated. Two bootstrap procedures are employed in the simulation study,where the latter bootstrap from Zeng and Lin(2008) is 4000 times faster than the former naive one, and the numerical results in both methods show that our estimating approach works well. A real data example is used to illustrate its application.

Monotone rank estimation of transformation models with length-biased and right-censored data

This paper considers the monotonic transformation model with an unspecified transformation function and an unknown error function, and gives its monotone rank estimation with length-biased and rightcensored data. The estimator is shown to be√n-consistent and asymptotically normal. Numerical simulation studies reveal good finite sample performance and the estimator is illustrated with the Oscar data set. The variance can be estimated by a resampling method via perturbing the U-statistics objective function repeatedly.

Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages

Aims In ecology and conservation biology,the number of species counted in a biodiversity study is a key metric but is usually a biased underestimate of total species richness because many rare species are not detected.Moreover,comparing species richness among sites or samples is a statistical challenge because the observed number of species is sensitive to the number of individuals counted or the area sampled.For individual-based data,we treat a single,empirical sample of species abundances from an investigator-defined species assemblage or community as a reference point for two estimation objectives under two sampling models:estimating the expected number of species(and its unconditional variance)in a random sample of(i)a smaller number of individuals(multinomial model)or a smaller area sampled(Poisson model)and(ii)a larger number of individuals or a larger area sampled.For sample-based incidence(presence–absence)data,under a Bernoulli product model,we treat a single set of species incidence frequencies as the reference point to estimate richness for smaller and larger numbers of sampling units.Methods The first objective is a problem in interpolation that we address with classical rarefaction(multinomial model)and Coleman rarefaction(Poisson model)for individual-based data and with sample-based rarefaction(Bernoulli product model)for incidence frequencies.The second is a problem in extrapolation that we address with sampling-theoretic predictors for the number of species in a larger sample(multinomial model),a larger area(Poisson model)or a larger number of sampling units(Bernoulli product model),based on an estimate of asymptotic species richness.Although published methods exist for many of these objectives,we bring them together here with some new estimators under a unified statistical and notational framework.This novel integration of mathematically distinct approaches allowed us to link interpolated(rarefaction)curves and extrapolated curves to plot a unified species accumulation curve for empirical examples.We provide new,unconditional variance estimators for classical,individual-based rarefaction and for Coleman rarefaction,long missing from the toolkit of biodiversity measurement.We illustrate these methods with datasets for tropical beetles,tropical trees and tropical ants.Important Findings Surprisingly,for all datasets we examined,the interpolation(rarefaction)curve and the extrapolation curve meet smoothly at the reference sample,yielding a single curve.Moreover,curves representing 95%confidence intervals for interpolated and extrapolated richness estimates also meet smoothly,allowing rigorous statistical comparison of samples not only for rarefaction but also for extrapolated richness values.The confidence intervals widen as the extrapolation moves further beyond the reference sample,but the method gives reasonable results for extrapolations up to about double or triple the original abundance or area of the reference sample.We found that the multinomial and Poisson models produced indistinguishable results,in units of estimated species,for all estimators and datasets.For sample-based abundance data,which allows the comparison of all three models,the Bernoulli product model generally yields lower richness estimates for rarefied data than either the multinomial or the Poisson models because of the ubiquity of non-random spatial distributions in nature.

Rosenthal's Inequalities for Asymptotically Almost Negatively Associated Random Variables Under Upper Expectations

In this paper, the authors generalize the concept of asymptotically almost negatively associated random variables from the classic probability space to the upper expectation space. Within the framework, the authors prove some different types of Rosenthal's inequalities for sub-additive expectations. Finally, the authors prove a strong law of large numbers as the application of Rosenthal's inequalities.

Robust estimation for partially linear models with large-dimensional covariates

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a nonconcave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n1/2), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance.Comprehensive simulation studies are carried out and an application is presented to examine the fnite-sample performance of the proposed procedures.

Semiparametric estimation of a Box-Cox transformation model with varying coefficients model

This paper considers the estimation of a Box-Cox transformation model with varying coefficient. A two-step approach is proposed in which the first step estimates the varying coefficients nonparametrically for any given parameter α in the transformation function. Then a one-dimensional search of α has been employed based on some least absolute deviation criterion function. The validity of our estimator does not require independence assumption thus is robust to the conditional heteroscedasticity. A simulation study shows a reasonably well finite sample performance. Additionally, a comprehensive empirical study has been carefully examined.

Asymptotic properties of Lasso in high-dimensional partially linear models

We study the properties of the Lasso in the high-dimensional partially linear model where the number of variables in the linear part can be greater than the sample size.We use truncated series expansion based on polynomial splines to approximate the nonparametric component in this model.Under a sparsity assumption on the regression coefficients of the linear component and some regularity conditions,we derive the oracle inequalities for the prediction risk and the estimation error.We also provide sufficient conditions under which the Lasso estimator is selection consistent for the variables in the linear part of the model.In addition,we derive the rate of convergence of the estimator of the nonparametric function.We conduct simulation studies to evaluate the finite sample performance of variable selection and nonparametric function estimation.

Quantile Regression for Right-Censored and Length-Biased Data

Length-biased data arise in many important fields, including epidemiological cohort studies, cancer screening trials and labor economics. Analysis of such data has attracted much attention in the literature. In this paper we propose a quantile regression approach for analyzing right-censored and length-biased data. We derive an inverse probability weighted estimating equation corresponding to the quantile regression to correct the bias due to length-bias sampling and informative censoring. This method can easily handle informative censoring induced by length-biased sampling. This is an appealing feature of our proposed method since it is generally difficult to obtain unbiased estimates of risk factors in the presence of length-bias and informative censoring. We establish the consistency and asymptotic distribution of the proposed estimator using empirical process techniques. A resampling method is adopted to estimate the variance of the estimator. We conduct simulation studies to evaluate its finite sample performance and use a real data set to illustrate the application of the proposed method.