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Statistics & Probability
Hailin Feng, Qianqian Luo
Summary: Quantile regression is highly flexible in describing the relationship between covariates and response variables. This paper introduces a new weighted quantile regression method for nonlinear models with randomly censored responses, which can handle more complex quantile regression models within the range of (0, 1). The consistency, asymptotic normality, and finite sample performance of the proposed estimator are also examined in this study.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2021)
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Automation & Control Systems
Sandra Vasquez, Michel Kinnaert, Rik Pintelon
Summary: This paper investigates the consistency and asymptotic normality of data concatenation for identifying Linear Time-Invariant models. The results show that certain model structures are consistently estimated and the estimated parameters are asymptotically normally distributed when input signals are persistently exciting. However, some model structures exhibit a bias in the estimated parameters, which asymptotically disappears for longer records.
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Mathematics, Applied
Siddhartha Chakraborty, Biswabrata Pradhan
Summary: This study proposes two estimators of cumulative residual extropy and compares their performance with an existing kernel based estimator through simulation. The asymptotic properties of the estimators are examined, and two estimators of quantile cumulative residual extropy measure are also proposed, demonstrating their consistency and asymptotic normality. The proposed estimators show good performance through analysis of two data sets.
RICERCHE DI MATEMATICA
(2023)
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Economics
Yuya Sasaki, Yulong Wang
Summary: This article proposes a method of diagnostic testing for key assumptions in econometric analyses, with applications to both simulated and real datasets.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2023)
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Mathematics, Interdisciplinary Applications
Yanjin Peng, Lei Wang
Summary: In this paper, the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression models in the presence of streaming data. The method modifies the quantile regression score function and carries out an online debiasing procedure to effectively estimate the quantile regression models in streaming data and establish the asymptotic normality of the resulting estimator.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2023)
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Statistics & Probability
Shaojun Guo, Yu Han, Qingsong Wang
Summary: This article revisits the problem of constructing better nonparametric confidence intervals for the conditional quantile function from an optimization perspective. A fully data-driven bias correction procedure based on local polynomial smoothing is applied, and an asymptotic framework is used to consider the estimated bias effect. New asymptotic normality and variance formulas are derived, leading to the construction of two new pointwise confidence intervals through resampling strategies. Extensive simulation studies show that the proposed confidence intervals outperform competitors in terms of coverage probabilities and interval lengths, and are not sensitive to bandwidth selection.
Article
Computer Science, Interdisciplinary Applications
Meiling Hao, Yuanyuan Lin, Guohao Shen, Wen Su
Summary: This paper investigates global estimation in semiparametric quantile regression models. It proposes an integrated quantile regression loss function with penalization for estimating unknown functional parameters. The first step is to obtain a vector-valued functional Bahadur representation of the resulting estimators, followed by deriving the asymptotic distribution of the proposed infinite-dimensional estimators. Additionally, a resampling approach that generalizes the minimand perturbing technique is used to construct confidence intervals and conduct hypothesis testing. Extensive simulation studies demonstrate the effectiveness of the proposed method, and applications to real estate dataset and world happiness report data are provided.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2023)
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Statistics & Probability
Balasubramaniam Natarajan, Weixing Song
Summary: In this paper, an adaptive nonparametric regression estimation procedure with a finite interval support for the covariate is proposed. The kernel estimator based on the Beta density function is investigated for its large sample properties, including asymptotic normality and uniform convergence. Guidelines for bandwidth selection using data-driven approach are suggested. The finite sample performance of the proposed estimator is evaluated through simulation study and real data application.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2022)
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Computer Science, Information Systems
Fode Zhang, Jialiang Li, Hon Keung Tony Ng
Summary: This paper proposes a parameter estimation method that minimizes the f-divergence between two probability distributions and explores the statistical properties of the estimator. The effectiveness of the proposed method is demonstrated through a case study on degradation modeling and analysis of real data.
IEEE TRANSACTIONS ON INFORMATION THEORY
(2022)
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Mathematics, Applied
Xianbin Chen, Juliang Yin
Summary: This paper studies the problem of simultaneous variable selection and estimation for longitudinal ordinal data with high-dimensional covariates. The penalized generalized estimation equation (GEE) method is used to obtain asymptotic properties for these data. The main result shows that under appropriate conditions, all covariates with zero coefficients can be examined simultaneously, and the estimator of non-zero coefficients exhibits asymptotic Oracle properties. Monte Carlo studies are conducted to demonstrate the theoretical analysis.
Article
Economics
Francisco Blasques, Siem Jan Koopman, Marc Nientker
Summary: This paper proposes a time-varying parameter model to describe the formation and burst of bubbles in financial and economic time series. The model can predict the emergence of bubbles and extract the unobserved bubble process from observed data through parameter estimation and an implied filter. The finite-sample properties of the estimator are examined through Monte Carlo simulations, and the model's superiority is demonstrated in a financial application involving the Bitcoin/US dollar exchange rate.
JOURNAL OF ECONOMETRICS
(2022)
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Mathematics, Interdisciplinary Applications
Lei Wang, Siying Sun, Zheng Xia
Summary: This paper introduces an empirical likelihood-based inference for parameters defined by the general estimating equations, showing consistency and asymptotic normality of the resulting estimator. The authors propose a two-stage estimation procedure using dimension-reduced kernel estimators and AIPW-MI methods, demonstrating the finite-sample performance through simulation and application to HIV-CD4 data.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2021)
Article
Statistics & Probability
Cui-Juan Kong, Han-Ying Liang, Guo-Liang Fan
Summary: In this paper, we focus on inferring the conditional quantile difference (CQD) for a left-truncated and right-censored model. We construct the local conditional likelihood function, the local likelihood ratio function, and the smoothed local log-likelihood ratio (log-SLL) of the CQD based on the observed data. Furthermore, we define the maximum local likelihood estimator of the CQD from the log-SLL. We establish the asymptotic normality of the defined estimator under the assumption of a sequence of stationary alpha-mixing random variables and prove the Wilks' theorem of adjusted log-SLL. Additionally, we define another estimator of the CQD based on the product-limit estimator of the conditional distribution function and provide its asymptotic normality. We also conduct simulation studies and real data analysis to examine the finite sample behavior of the proposed methods.
Article
Statistics & Probability
H. Benchoulak, M. Boukeloua, F. Messaci
Summary: We introduce a double kernel conditional mode estimator for doubly censored response variables. Through the estimation of R and L, we establish the uniform almost sure convergence of the conditional density estimator and deduce the consistency and asymptotic normality of the conditional mode estimator. Simulation studies and real data analysis demonstrate the quality and performance of the proposed estimator.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2022)
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Automation & Control Systems
Qiuping Wang, Ting Yan, Binyan Jiang, Chenlei Leng
Summary: This paper introduces the characteristics and privacy risks of two-mode network data, and proposes a weak notion of edge differential privacy for releasing the degree sequence of these networks. The consistency and asymptotic normality of two differential privacy estimators are established, and an efficient algorithm for generating synthetic bipartite graphs is developed. Numerical simulations and real data applications verify the usefulness of the proposed method.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
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Behavioral Sciences
Andre Ancel, Caroline Gilbert, Nicolas Poulin, Michael Beaulieu, Bernard Thierry
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Multidisciplinary Sciences
Valerie Dufour, Nicolas Poulin, Charlotte Cure, Elisabeth H. M. Sterck
SCIENTIFIC REPORTS
(2015)
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Biodiversity Conservation
Eugenie Schwoertzig, Nicolas Poulin, Laurent Hardion, Michele Trernolieres
ECOLOGICAL INDICATORS
(2016)
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Radiology, Nuclear Medicine & Medical Imaging
Philippe Meyer, Claudine Niederst, Maximilien Scius, Delphine Jarnet, Nicolas Dehaynin, Matthieu Gantier, Waisse Waissi, Nicolas Poulin, Diran Karamanoukian
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2016)
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Multidisciplinary Sciences
Charlotte Canteloup, Emilie Piraux, Nicolas Poulin, Helene Meunier
Meeting Abstract
Oncology
J. Clavier, D. Antoni, N. Bauer, F. Guillerme, P. Truntzer, D. Atlani, S. Guihard, A. Lahlou, M. Pop, S. Thiriat, C. Vigneron, N. Poulin, G. Noel
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2014)
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Marine & Freshwater Biology
I. Zimmer, Y. Ropert-Coudert, N. Poulin, A. Kato, A. Chiaradia
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Multidisciplinary Sciences
Francois Criscuolo, Candide Font-Sala, Frederic Bouillaud, Nicolas Poulin, Marie Trabalon
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Multidisciplinary Sciences
Cedric Zimmer, Mathieu Boos, Nicolas Poulin, Andrew Gosler, Odile Petit, Jean-Patrice Robin
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Infectious Diseases
Reinier J. M. Bom, Kalja van der Linden, Amy Matser, Nicolas Poulin, Maarten F. Schim van der Loeff, Bouko H. W. Bakker, Theodoor F. van Boven
BMC INFECTIOUS DISEASES
(2019)
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Behavioral Sciences
Caroline Gerard, Mathilde Valenchon, Nicolas Poulin, Odile Petit
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Ecology
Palmyre H. Boucherie, Nicolas Poulin, Valerie Dufour
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Behavioral Sciences
Charlotte Canteloup, Isis Poitrasson, James R. Anderson, Nicolas Poulin, Helene Meunier
Meeting Abstract
Zoology
Isis Poitrasson, Charlotte Canteloup, James R. Anderson, Nicolas Poulin, Helene Meunier
FOLIA PRIMATOLOGICA
(2017)
Article
Statistics & Probability
Tsung- Lin, Wan-Lun Wang
Summary: This paper derives explicit expressions for the moments of truncated multivariate normal/independent distributions with supports confined within a hyper-rectangle. A Monte Carlo experiment is conducted to validate the proposed formulae for five selected members of the distributions.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
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Statistics & Probability
Tao Qiu, Qintong Zhang, Yuanyuan Fang, Wangli Xu
Summary: This article introduces a method for testing the homogeneity of two random vectors. The method involves selecting two subspaces and projecting them onto one-dimensional spaces, using the Cramer-von Mises distance to construct the test statistic. The performance is enhanced by repeating this procedure and the effectiveness is demonstrated through numerical simulations.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Statistics & Probability
Alfredo Alegria, Xavier Emery
Summary: This study contributes to covariance modeling by proposing new parametric families of isotropic matrix-valued functions that exhibit non-monotonic behaviors, such as hole effects and cross-dimples. The benefit of these models is demonstrated on a bivariate dataset of airborne particulate matter concentrations.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
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Statistics & Probability
Kento Egashira, Kazuyoshi Yata, Makoto Aoshima
Summary: This study investigates the asymptotic properties of hierarchical clustering in different settings, including high-dimensional, low-sample-size scenarios. The results show that hierarchical clustering exhibits good asymptotic properties under practical settings for high-dimensional data. The study also extends the analysis to consider scenarios where both the dimension and sample size approach infinity, and generalizes the concept of populations in multiclass HDLSS settings.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
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Statistics & Probability
Marlene Baumeister, Marc Ditzhaus, Markus Pauly
Summary: This paper introduces a more robust multivariate analysis method by using general quantiles, particularly the median, instead of the traditional mean, and applies and validates this method on various factorial designs. The effectiveness of this method is demonstrated through theoretical and simulation studies on small and moderate sample sizes.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
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Statistics & Probability
Chuancun Yin, Narayanaswamy Balakrishnan
Summary: The family of multivariate skew-normal distributions has interesting properties, which also hold for a general class of skew-elliptical distributions.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
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Statistics & Probability
Gaspard Bernard, Thomas Verdebout
Summary: In this paper, we address the problem of testing the relationship between the eigenvalues of a scatter matrix in an elliptical distribution. Using the Le Cam asymptotic theory, we show that the non-specification of nuisance parameters has an asymptotic cost for testing the relationship. We also propose a distribution-free signed-rank test for this problem.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
