In this paper, we consider the estimation of common breaks for linear panel data models by means of screening and ranking algorithm. For static and dynamic panel data models, we estimate the regression coefficients us...
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In this paper, we consider the estimation of common breaks for linear panel data models by means of screening and ranking algorithm. For static and dynamic panel data models, we estimate the regression coefficients using covariance estimation and generalized method of moments, respectively, and apply a screening and ranking algorithm on this basis. The possible break points are first screened by constructing local statistics based on the coefficient estimators, then further screened by the thresholding rule, and finally the final break points are screened by the information criterion. Monte Carlo simulations demonstrate that the proposed methods work well in finite samples. We apply the screening and ranking algorithm to study the influence of rural residents' consumption demand on China's economic growth using a panel of 31 provinces from 2005 to 2023 and find a break point in the model.
DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed a...
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DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed and applied to detect CNVs based on data that generated by genome analysis platforms. However, most algorithms are computationally intensive with complexity at least O(n(2)), where n is the number of probes in the experiments. Moreover, the theoretical properties of those existing methods are not well understood. A faster and better characterized algorithm is desirable for the ultra high throughput data. In this study, we propose the screening and ranking algorithm (SaRa) which can detect CNVs fast and accurately with complexity down to O(n). In addition, we characterize theoretical properties and present numerical analysis for our algorithm.
Multiple change-points estimation in panel data models is one of the popular topics in statistics. In this article, we investigate the multiple change-points estimation in the mean of panel data model based on a scree...
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Multiple change-points estimation in panel data models is one of the popular topics in statistics. In this article, we investigate the multiple change-points estimation in the mean of panel data model based on a screening and ranking algorithm. Firstly, the possible change-points are initially screened based on local statistics. Secondly, the threshold is used to further screen the change-points. Finally, the final change-points are screened out using the information criterion. Furthermore, the consistency of the change-point estimators is proved. The Monte Carlo simulation results show that the proposed method can estimate the number and locations of change-points accurately even if the error terms are serially correlated or cross-sectionally correlated, and finally the method is used to analyze the annual GDP growth rate data of 47 countries in the word from 1971 to 2019 to illustrate the effectiveness of the method.
This study considers the problem of multiple change-points detection. For this problem, we develop an objective Bayesian multiple change-points detection procedure in a normal model with heterogeneous variances. Our B...
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This study considers the problem of multiple change-points detection. For this problem, we develop an objective Bayesian multiple change-points detection procedure in a normal model with heterogeneous variances. Our Bayesian procedure is based on a combination of binary segmentation and the idea of the screening and ranking algorithm (Niu and Zhang in Ann Appl Stat 6:1306-1326, 2012). Using the screening and ranking algorithm, we can overcome the drawbacks of binary segmentation, as it cannot detect a small segment of structural change in the middle of a large segment or segments of structural changes with small jump magnitude. We propose a detection procedure based on a Bayesian model selection procedure to address this problem in which no subjective input is considered. We construct intrinsic priors for which the Bayes factors and model selection probabilities are well defined. We find that for large sample sizes, our method based on Bayes factors with intrinsic priors is consistent. Moreover, we compare the behavior of the proposed multiple change-points detection procedure with existing methods through a simulation study and two real data examples.
Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and un...
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Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.
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