A Semiparametric Change-Point Regression Model for Longitudinal Observations

Xing, Haipeng; Ying, Zhiliang

doi:10.1080/01621459.2012.712425

Cited by 11 publications

(8 citation statements)

References 56 publications

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“…Each of the stringency index, hospital beds and diabetes prevalence have no significant effect on both of 0.5 th quantile and 0.8 th quantile of new monthly deaths per million due to COVID-19 . Table (5) presents the AIC values for the proposed model and the Li, et. al [7] model at τ =0.5 and 0.8.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

“…Table (3) presents the descriptive statistics of the variables in the study. Figure (5) presents the profile plot of the death rate for all countries over time.…”

Section: Datamentioning

confidence: 99%

“…In Bayesian framework, see for example, Ghosh and Vaida [3], Yang and Gao [2], McLain, and Albert [4]. Xing and Ying [5] propose a semi-parametric changepoint regression model for longitudinal data. Lai and Albert [6] propose a linear mixed effects modeling framework for multiple change points in longitudinal Gaussian data.…”

Section: Introductionmentioning

confidence: 99%

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A Mixed Effects Changepoint Quantile Regression Model for Longitudinal Data

Gad¹,

Ibrahim²,

Abd-Rabou³

2022

Preprint

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<p> The longitudinal individual response profiles could exhibit a mixture of two or more phases of increase or decrease in trend throughout the follow up period, with one or more unknown transition points usually referred to as breakpoints or change points. The existence of such unknown point disturbs the sample characteristics, so the detection and estimation of these points is crucial. Most of the proposed statistical methods in literature, for detecting and estimating change points, assume distributional assumption that may not hold. A good alternative in this case is to use a robust approach which is the quantile regression model. There are trials in the literature to deal with quantile regression models with a change point. These trials ignore the within subject dependence of longitudinal data. In this paper we propose a mixed effect quantile regression model with a change point to account for dependence structure in the longitudinal data. Fixed effect parameters, in addition to the location of the change point, are estimated using profile estimation method. The stochastic approximation EM algorithm is proposed to estimate the fixed effect parameters exploiting the link between asymptotic Laplace distribution and the quantile regression. In addition, the location of the change point is estimated using the usual optimization methods. A simulation study shows that the proposed estimation and inferential procedures perform reasonably well in finite samples. The practical use of the proposed model is illustrated using a COVID-19 data. The data focus on the effect of global economic and health factors on the monthly death rate due to COVID-19 during from the 1st of April 2020 till the 31st of April 2021. </p>

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Section: Data Analysis and Resultsmentioning

confidence: 99%

“…Table (3) presents the descriptive statistics of the variables in the study. Figure (5) presents the profile plot of the death rate for all countries over time.…”

Section: Datamentioning

confidence: 99%

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A Mixed Effects Changepoint Quantile Regression Model for Longitudinal Data

Gad¹,

Ibrahim²,

Abd-Rabou³

2022

Preprint

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“…Dividing the whole dataset into different clusters, we assume sequences in a cluster all have the same bisulfite accessible regions, and when they are added up, the sites are binomial distributed. Using the multiple change-point method [70,71], we develop a Bayesian segmentation model to detect bisulfite accessible regions.…”

Section: Bisulfite Data Analysis Using Bayesian Modelmentioning

confidence: 99%

A Bayesian model based computational analysis of the relationship between bisulfite accessible single-stranded DNA in chromatin and somatic hypermutation of immunoglobulin genes

Duan

et al. 2021

PLoS Comput Biol

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The B cells in our body generate protective antibodies by introducing somatic hypermutations (SHM) into the variable region of immunoglobulin genes (IgVs). The mutations are generated by activation induced deaminase (AID) that converts cytosine to uracil in single stranded DNA (ssDNA) generated during transcription. Attempts have been made to correlate SHM with ssDNA using bisulfite to chemically convert cytosines that are accessible in the intact chromatin of mutating B cells. These studies have been complicated by using different definitions of “bisulfite accessible regions” (BARs). Recently, deep-sequencing has provided much larger datasets of such regions but computational methods are needed to enable this analysis. Here we leveraged the deep-sequencing approach with unique molecular identifiers and developed a novel Hidden Markov Model based Bayesian Segmentation algorithm to characterize the ssDNA regions in the IGHV4-34 gene of the human Ramos B cell line. Combining hierarchical clustering and our new Bayesian model, we identified recurrent BARs in certain subregions of both top and bottom strands of this gene. Using this new system, the average size of BARs is about 15 bp. We also identified potential G-quadruplex DNA structures in this gene and found that the BARs co-locate with G-quadruplex structures in the opposite strand. Using various correlation analyses, there is not a direct site-to-site relationship between the bisulfite accessible ssDNA and all sites of SHM but most of the highly AID mutated sites are within 15 bp of a BAR. In summary, we developed a novel platform to study single stranded DNA in chromatin at a base pair resolution that reveals potential relationships among BARs, SHM and G-quadruplexes. This platform could be applied to genome wide studies in the future.

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“…The proposed model has two types of complexities, one is the semiparametric feature of the intensity functions, and the other is the nonlinear dynamics of regression coefficients θ (i,j) (t). To develop an inference procedure, we borrow the idea of mixtured estimating equations developed by [26]. Specifically, we first consider an estimating equation for the case that there are no structural breaks in θ (i,j) (t) during the period (t * , t * ), we then link all estimating-equation-based estimates by mixture weights that can be computed explicitly.…”

Section: Inference Proceduresmentioning

confidence: 99%

Firm's Credit Risk in the Presence of Market Structural Breaks

Xing¹,

Yu²

2018

Preprint

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Various sudden shifts in financial market conditions over the past decades have demonstrated the significant impact of market structural breaks on firms' credit behavior. To characterize such effect quantitatively, we develop a continuous-time modulated Markov model for firms' credit rating transitions with the possibility of market structural breaks. The model takes a semi-parametric multiplicative regression form, in which the effects of firms' observable covariates and macroeconomic variables are represented parametrically and nonparametrically, respectively, and the frailty effects of unobserved firm-specific and market-wide variables are incorporated via the integration form of the model assumption. We further develop a mixtured-estimating-equation approach to make inference on the effect of market variations, baseline intensities of all firms' credit rating transitions, and rating transition intensities for each individual firm. We then use the developed model and inference procedure to analyze the monthly credit rating of U.S. firms from January 1986 to December 2012, and study the effect of market structural breaks on firms' credit rating transitions.

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A Semiparametric Change-Point Regression Model for Longitudinal Observations

Cited by 11 publications

References 56 publications

A Mixed Effects Changepoint Quantile Regression Model for Longitudinal Data

A Mixed Effects Changepoint Quantile Regression Model for Longitudinal Data

A Bayesian model based computational analysis of the relationship between bisulfite accessible single-stranded DNA in chromatin and somatic hypermutation of immunoglobulin genes

Firm's Credit Risk in the Presence of Market Structural Breaks

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