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 relation- ships 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 infor- mation 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 → ∞.

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.

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.

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.

Semiparametric estimation of average treatment effect through a random coefficient dummy endogenous variable model

This paper provides an estimation procedure for average treatment effect through a random coefficient dummy endogenous variable model. A leading example of the model is estimating the effect of a training program on earnings. The model is composed of two equations:an outcome equation and a decision equation.Given the linear restriction in outcome and decision equations,Chen(1999) provided a distribution-free estimation procedure under conditional symmetric error distributions. In this paper we extend Chen's estimator by relaxing the linear index into a nonparametric function,which greatly reduces the risk of model misspecification. A two-step approach is proposed:the first step uses a nonparametric regression estimator for the decision variable,and the second step uses an instrumental variables approach to estimate average treatment effect in the outcome equation. The proposed estimator is shown to be consistent and asymptotically normally distributed. Furthermore,we investigate the finite performance of our estimator by a Monte Carlo study and also use our estimator to study the return of college education in different periods of China. The estimates seem more reasonable than those of other commonly used estimators.

Estimation of Partially Specified Spatial Panel Data Models with Random-Effects

In this article, we study estimation of a partially specified spatial panel data linear regression with random-effects. Under the conditions of exogenous spatial weighting matrix and exogenous regressors, we give an instrumental variable estimation. Under certain sufficient assumptions, we show that the proposed estimator for the finite dimensional parameter is root-N consistent and asymptotically normally distributed and the proposed estimator for the unknown function is consistent and asymptotically distributed. Consistent estimators for the asymptotic variance-covariance matrices of both the parametric and unknown components are provided. The Monte Carlo simulation results verify our theory and suggest that the approach has some practical value.

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.

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.

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.

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.

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.

Semiparametric quantile-difference estimation for length-biased and right-censored data

Prevalent cohort studies frequently involve length-biased and right-censored data, a fact that has drawn considerable attention in survival analysis. In this article, we consider survival data arising from lengthbiased sampling, and propose a new semiparametric-model-based approach to estimate quantile differences of failure time. We establish the asymptotic properties of our new estimators theoretically under mild technical conditions, and propose a resampling method for estimating their asymptotic variance. We then conduct simulations to evaluate the empirical performance and efficiency of the proposed estimators, and demonstrate their application by a real data analysis.

Feature Selection, Deep Neural Network and Trend Prediction

The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis(SRA)algorithm to constrained SRA(c SRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network(DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.

The Superiority of Bayes Estimators in a Multivariate Linear Model with Respect to Normal-Inverse Wishart Prior

In this paper, the multivariate linear model Y = XB＋e, e ～ Nm×k（0, ImΣ） is considered from the Bayes perspective. Under the normal-inverse Wishart prior for （BΣ）, the Bayes estimators are derived. The superiority of the Bayes estimators of B and Σ over the least squares estimators under the criteria of Bayes mean squared error （BMSE） and Bayes mean squared error matrix （BMSEM） is shown. In addition, the Pitman Closeness （PC） criterion is also included to investigate the superiority of the Bayes estimator of B.

SMOOTH ESTIMATION OF ROC CURVE IN THE PRESENCE OF AUXILIARY INFORMATION

接收装置操作特征(巨鸟) 曲线经常被用来在药学习并且比较二样品的问题。当更多的信息可能比其它在一个处理上是可得到的时，如果辅助人口信息被考虑，一个人能改进巨鸟曲线的评估者。作者证明实验可能性的方法能自然地被改编做辅助信息的有效使用到如此的问题。作者在医药研究与一些辅助信息为巨鸟曲线建议一个弄平的实验可能性的评估者。没有任何辅助信息，建议估计是比那些巨鸟评估者更有效的，在比较 asymptotic 变化和均方差(MSE ) 的意义。为巨鸟曲线的实验可能性的评价的一些 asymptotic 性质被建立。模拟研究被介绍表明建议评估者的表演。