Model update mechanism for mean-shift tracking

In order to solve the model update problem in mean-shift based tracker, a novel mechanism is proposed. Kalman filter is employed to update object model by filtering object kernel-histogram using previous model and current candidate. A self-tuning method is used for adaptively adjust all the parameters of the filters under the analysis of the filtering residuals. In addition, hypothesis testing servers as the criterion for determining whether to accept filtering result. Therefore, the tracker has the ability to handle occlusion so as to avoid over-update. The experimental results show that our method can not only keep up with the object appearance and scale changes but also be robust to occlusion.

Outlier Rejecting Multirate Model for State Estimation

Wavelet transform was introduced to detect and eliminate outliers in time-frequency domain. The outlier rejection and multirate information extraction were initially incorporated by wavelet transform, a new outlier rejecting multirate model for state estimation was proposed. The model is applied to state estimation with interacting multiple model, as the outlier is eliminated and more reasonable multirate information is extracted, the estimation accuracy is greatly enhanced. The simulation results prove that the new model is robust to outliers and the estimation performance is significantly improved.

The assessment of the outliers of logistic regression model and its clinical application

On the basis of the newly developed regression diagnostic analysis, the diagnostic method with the assessment of the outliers of the logistic regression model was set up and it was used to analyze the prognosis of the patients with acute lymphatic leukemia.

OUTLIER TEST IN RANDOMIZED LINEAR MODEL

In this papert we give an approach for detecting one or more outliers inrandomized linear model.The likelihood ratio test statistic and its distributions underthe null hypothesis and the alternative hypothesis are given. Furthermore,the robustnessof the test statistic in a certain sense is proved. Finally,the optimality properties of thetest are derived.

Wavelet Based Detection of Outliers in Volatility Time Series Models

We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.The procedure focuses on the analysis of residuals obtained from a model fit,and applied to the Generalized Autoregressive Conditional Heteroskedasticity(GARCH)like model,but not limited to these models.We apply the Maximal-Overlap Discrete Wavelet Transform(MODWT)to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers.Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform(DWT).The series sample size does not need to be a power of 2 and the transform can explore any wavelet filter and be run up to the desired level.Simulated wavelet quantiles from a Normal and Student t-distribution are used as threshold for the maximum of the absolute value of wavelet coefficients.The performance of the procedure is illustrated and applied to two real series:the closed price of the Saudi Stock market and the S&P 500 index respectively.The efficiency of the proposed method is demonstrated and can be considered as a distinct important addition to the existing methods.

Application of Iterative Approaches in Modeling the Efficiency of ARIMA-GARCH Processes in the Presence of Outliers

The study explored both Box and Jenkins, and iterative outlier detection procedures in determining the efficiency of ARIMA-GARCH-type models in the presence of outliers using the daily closing share price returns series of four prominent banks in Nigeria (Skye (Polaris) bank, Sterling bank, Unity bank and Zenith bank) from January 3, 2006 to November 24, 2016. The series consists of 2690 observations for each bank. The data were obtained from the Nigerian Stock Exchange. Unconditional variance and kurtosis coefficient were used as criteria for measuring the efficiency of ARIMA-GARCH-type models and our findings revealed that kurtosis is a better criterion (as it is a true measure of outliers) than the unconditional variance (as it can be depleted or amplified by outliers). Specifically, the strength of this study is in showing the applicability and relevance of iterative methods in time series modeling.

A Robust Interacting Multisensor State Fusion Based on Adaptive Outlier Controlling Multirate Model

An adaptive outlier controlling multirate model based on Hong’s multirate kinetic model was represented in order to resist the outliers and utilize their useful information. Wavelet transform was introduced to detect and control the outliers. The multirate information extraction and the controlling of outliers were properly integrated to establish an adaptive outlier controlling multirate model. The proposed model was applied to multisensor state fusion with interacting multiple model (IMM), and a robust interacting multisensor state fusion algorithm was established based on adaptive outlier controlling multirate model. The Monte-Carlo simulation shows that it could improve the accuracy of fusion estimation by 70% compared to Hong’s algorithm and at least 14% to Xiao’s algorithm.

Using Residual Estimators to Detect Outliers and Potential Controlling Observations in Structural Equation Modelling: QQ Plot Approach

The structural equation model (SEM) concept is generally influenced by the presence of outliers and controlling variables. To a very large extent, this could have consequential effects on the parameters and the model fitness. Though previous researches have studied outliers and controlling observations from various perspectives including the use of box plots, normal probability plots, among others, the use of uniform horizontal QQ plot is yet to be explored. This study is, therefore, aimed at applying uniform QQ plots to identifying outliers and possible controlling observations in SEM. The results showed that all the three methods of estimators manifest the ability to identify outliers and possible controlling observations in SEM. It was noted that the Anderson-Rubin estimator of QQ plot showed a more efficient or visual display of spotting outliers and possible controlling observations as compared to the other methods of estimators. Therefore, this paper provides an efficient way identifying outliers as it fragments the data set.

OUTLIER DETECTION AND RELIABILITY OF ADJUSTMENT MODELS WITH SINGULAR COVARIANCE MATRICES

Up to now,outlier detection and reliability theory are generallybased on the regular Gauss-Markov models,in which the covariance matrix of ob-servations is positively definite.For the adjustment models with singular covari-ance matrix,the statistics for outlier detection are derived by the authors.Thecorresponding reliability theory is developed.And the application of the theory isdemonstrated with a practical example.

一种新的Mean-Shift对象跟踪方法

由于存在外点干扰,传统的Mean-Shift对象跟踪方法不能有效的跟踪尺寸逐步变大的目标。猜想利用对象初始模型能有效排除外点对跟踪结果的干扰,在此基础上本文提出了一种利用对象初始模型的候选目标模型和相似度测量方法;为了使内核窗口中心及大小与对象形心及大小一致,根据内核窗口外一定宽度子带内像素分布情况,动态调整内核窗口大小和位置。通过对尺寸逐渐变大的汽车进行跟踪,验证了算法的有效性,同时试验结果也证明新算法具有更低的计算复杂度。

来自于Multiple-Outlier模型的最小次序统计量序性质（英文）

本文中,我们研究来自于两个multiple-outlier模型的最小次序统计量的随机比较,其中两个模型中独立同分布的随机变量个数不同.令X_(1:n)(p,q)和X_(1:n~*)(p~*,q~*)分别表示来自于X_1,…,X_p,X_(p+1),…,X_n和X_1,…,X_(p),X_(p~*+1),…,X_(n)的最小次序统计量,这里q=n-p,q~*=n~*-p~*.在参数(p,q)和(p~*,q~*)满足某些优化序条件下,我们根据普通随机序,失效率序和似然比序给出了X_(1:n)(p,q)和X_(1:n~*)(p~*,q~*)的序比较.

Application of the Improved Generalized Autoregressive Conditional Heteroskedast Model Based on the Autoregressive Integrated Moving Average Model in Data Analysis

This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model for the issue that financial product sales data have singular information when applying this model, and the improved outlier detection method was used to detect the location of outliers, which were processed by the iterative method. Secondly, in order to describe the peak and fat tail of the financial time series, as well as the leverage effect, this work used the skewed-t Asymmetric Power Autoregressive Conditional Heteroskedasticity model based on the Autoregressive Integrated Moving Average Model to analyze the sales data. Empirical analysis showed that the model considering the skewed distribution is effective.

Statistical Diagnostic for Varying-Coefficient Single-Index Models Based on Empirical Likelihood Method

Varying-coefficient single-index model( VCSIM) avoids the so-called "curse of dimensionality " and is flexible enough to include several important statistical models. This paper considers statistical diagnosis for VCSIM. First,the parametric estimation equation is established based on empirical likelihood. Then,some diagnosis statistics are defined. At last, an example is given to illustrate all the results.

双向分类随机可加效应模型中可加性异常值(outlier)的检验

在双向分类随机效应模型中，在实际应用时人们更感兴趣的是模型的可加性。此时异常值的出现是由于它偏离了模型的可加性假设。即数据中的少部分点偏离了模型的可加性。这在实际中是一个重要问题，同时也是本文所研究的内容。本文安排如下：第二节给出模型的介绍及问题的由来；第三节导出了可加性ｏｕｔｌｉｅｒ的检验方法及检验统计量的精确分布，并给出双向分类随机效应模型中可加性异常的检验方法；第四节对相关问题进行了讨论。

Identification Model for Needy Undergraduates Based on FFM

In recent years, as the enrollment rate of Chinese colleges has increased year by year, the identification of needy undergraduates has become increasingly important. However, the traditional way to identify college students with financial difficulties mainly relies on manual review and collective voting, which easily causes subjectivity and randomness. To alleviate the problem above, this paper establishes an automatic identification model for needy undergraduates based on the 1842 questionnaires collected from undergraduates in WHUT. Firstly, this paper filters the questionnaire preliminary using the local outlier factor algorithm. Secondly, this paper combines mutual information, Spearman rank correlation coefficient and distance correlation coefficient by rank-sum ratio to select features for eliminating noise from irrelevant features. Thirdly, this paper trains filed-aware factor machine model and compares it with other models, such as Logistic Regression, SVM, etc. Eventually, this paper finds that filed-aware factor machine performers much better than other models in the identification of needy undergraduates, and prominent features affecting the identification of needy undergraduates are the year of the family income, cost of living provided parents, etc.

Bayesian Diagnostic Checking of the Capital Asset Pricing Model

The capital asset pricing model (CAPM) is a commonly used regression model in finance to model stock returns. Bayesian methods have been developed for the CAPM to account for market fluctuations within the industry. However, a Bayesian model checking procedure is needed to assess the CAPM in terms of the usual regression model assumptions of independence, homogeneity of variance, and normality. This paper develops Bayesian residuals and Bayesian p-values to check these model assumptions as well as to suggest model extensions to the CAPM.

Local Empirical Likelihood Diagnosis of Varying Coefficient Density-Ratio Models Based on Case-Control Data

In this paper, a varying-coefficient density-ratio model for case-control studies is developed. We investigate the local empirical likelihood diagnosis of varying coefficient density-ratio model for case-control data. The local empirical log-likelihood ratios for the nonparametric coefficient functions are introduced. First, the estimation equations based on empirical likelihood method are established. Then, a few of diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation studies.

Empirical Likelihood Diagnosis of Modal Linear Regression Models

In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is introduced. First, the estimation equation based on empirical likelihood method is established. Then, some diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation study.

Estimation of Bounded Populations and Carrying Capacity with the Logistic Model

Estimation of bounded populations and carrying capacity in the presence of a sample frame is considered. Models based on Logistic model are proposed. Like the existing estimators, this estimation technique deals with initial condition and is based on yearly population totals in order to fit in a model within a given period of time in this study. The proposed Logistic model technique has shown to be efficient especially with large data. The empirical study indicated that the Logistic model is efficient and can estimate properly even in the presence of outliers.

Bootstrap Approaches to Autoregressive Model on Exchange Rates Currency

The use of historical data is important in making the predictions, for instance in the exchange rate. However, in the construction of a model, extreme data or dirtiness of data is inevitable. In this study, AR model is used with the exchange rate historical data (January 2007 until December 2007) for USD/MYR and is divided into 1-, 3- and 6-horizontal months respectively. Since the presence of extreme data will affect the accuracy of the results obtained in a prediction. Therefore, to obtain a more accurate prediction results, the bootstrap approach was implemented by hybrid with AR model coins as the Bootstrap Autoregressive model (BAR). The effectiveness of the proposed model is investigated by comparing the existing and the proposed model through the statistical performance methods which are RMSE, MAE and MAD. The comparison involves 1%, 5% and 10% for each horizontal month. The results showed that the BAR model performed better than the AR model in terms of sensitivity to extreme data, the accuracy of forecasting models, efficiency and predictability of the model prediction. In conclusion, bootstrap method can alleviate the sensitivity of the model to the extreme data, thereby improving the accuracy of forecasting model which also have high prediction efficiency and that can increase the predictability of the model.