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local regressions for decomposing CO2 and CH4 time-series in a semi-arid ecosystem

ATMOSPHERIC POLLUTION RESEARCH 2020年第1期11卷 213-223页

作者： Fernandez-Duque, Beatriz Perez, Isidro A. Angeles Garcia, M. Pardo, Nuria Luisa Sanchez, M. Univ Valladolid Dept Appl Phys Fac Sci Paseo Belen 7 E-47011 Valladolid Spain

local regressions have been widely employed for decomposing atmospheric data series. However, the use of local quadratic regressions is less extended. The current paper is grounded on the hypothesis that local linear regressions are able to capture CO2 and CH4 temporal evolution equally as well as quadratic linear regressions. Thus, the current paper pursues the following goals: (1) to quantify the temporal variability of both gases by using the local linear and local quadratic regression method;(2) to analyse the main statistics of the detrended series over time;(3) to compare results between the local linear and local quadratic regression method. Minimum mixing ratios in late summer and maximum in winter were detected for both gases. Atmospheric increases of an average of 1.98 ppm year(-1) for CO2 and 11 ppb year(-1) for CH4 were found when applying the local linear regression method. Alternatively, an increase of 2.24 ppm year(-1) for CO2 and around 10.34 ppb year(-1) for CH4 was obtained when the local quadratic regression method was applied. The Pearson correlation coefficients (0.21-0.40) showed acceptable values due to the large amount of available data. Statistically significant differences for the initial and the smoothed trend, as well as statistically significant differences for the seasonal component, were reported when comparing the local linear with the local quadratic method. Overall, both methods proved easy to apply and both provided acceptable data accuracy.

关键词： local linear regression local quadratic regression Bandwidth Detrended series r-Values

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Nonparametric bootstrap approach for unconditional risk mapping under heteroscedasticity

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SPATIAL STATISTICS 2020年 40卷

作者： Castillo-Paez, Sergio Fernandez-Casal, Ruben Garcia-Soidan, Pilar Univ Fuerzas Armadas ESPE Dept Ciencias Exactas Sangolqui Ecuador Univ A Coruna Dept Matemat La Coruna Spain Univ A Coruna Ctr Invest CITIC La Coruna Spain Univ Vigo Dept Estadist & Invest Operat Vigo Spain

The current work provides a nonparametric resampling procedure for approximating the (unconditional) probability that a spatial variable surpasses a prefixed threshold value. The existing approaches for the latter issue require assuming constant variance throughout the observation region, thus our proposal has been designed to be valid under heteroscedasticity of the spatial process. To develop the new methodology, nonparametric estimates of the variance and the semivariogram functions are computed by using bias-corrected residuals, which are then employed to derive bootstrap replicates for approximating the aforementioned risk. The performance of this mechanism is checked through numerical studies with simulated data, where a comparison with a semiparametric method is also included. In addition, the practical application of this approach is exemplified by estimating the risk of rainwater accumulation in the United States, during a specific period. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Heteroscedasticity local linear regression Resampling method

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Covariance function versus covariance matrix estimation in efficient semi-parametric regression for longitudinal data analysis

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JOURNAL OF MULTIVARIATE ANALYSIS 2022年 187卷 104900-104900页

作者： Jia, Shengji Zhang, Chunming Lu, Haoran Shanghai Lixin Univ Accounting & Finance Interdisciplinary Res Inst Data Sci Sch Stat & Math Shanghai 201209 Peoples R China Univ Wisconsin Dept Stat Madison WI 53706 USA Univ Georgia Dept Stat Athens GA 30602 USA

Improving estimation efficiency for regression coefficients is an important issue in the analysis of longitudinal data, which involves estimating the covariance matrix of the within-subject errors. In the balanced or nearly balanced setting, we can also regard the covariance matrix of the dependent errors as the bivariate covariance function evaluated at specific time points. In this paper, we compare the performance of the proposed regularized-covariance-function-based estimator and the conventional high-dimensional covariance matrix estimator of the within-subject errors. It shows that when the number p of the time points in each subject is large enough compared to the number n of the subjects, i.e., p >> n(1/4) log n, the estimation errors of the high-dimensional covariance matrix will be accumulated, therefore the error bound of the proposed regularized covariance-function-based estimator will be smaller than that of the high-dimensional covariance matrix estimator in Frobenius norm. We also assess the performance of these two estimators for the incomplete longitudinal data. All the comparisons and theoretical results are illustrated using both simulated and real data. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Covariance function High-dimensional covariance matrix local linear regression Method of regularization Profile weighted least squares Varying-coefficient partially linear model

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Nonparametric Estimation of the Conditional Distribution at regression Boundary Points

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AMERICAN STATISTICIAN 2020年第3期74卷 233-242页

作者： Das, Srinjoy Politis, Dimitris N. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Math La Jolla CA 92093 USA

Nonparametric regression is a standard statistical tool with increased importance in the Big Data era. Boundary points pose additional difficulties but local polynomial regression can be used to alleviate them. local linear regression, for example, is easy to implement and performs quite well both at interior and boundary points. Estimating the conditional distribution function and/or the quantile function at a given regressor point is immediate via standard kernel methods but problems ensue if local linear methods are to be used. In particular, the distribution function estimator is not guaranteed to be monotone increasing, and the quantile curves can "cross." In the article at hand, a simple method of correcting the local linear distribution estimator for monotonicity is proposed, and its good performance is demonstrated via simulations and real data examples. for this article are available online.

关键词： Kernel smoothing local linear regression local polynomial fitting Model-free prediction Point prediction

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A goodness-of-fit test for regression models with spatially correlated errors

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TEST 2020年第3期29卷 728-749页

作者： Meilan-Vila, Andrea Opsomer, Jean D. Francisco-Fernandez, Mario Crujeiras, Rosa M. Univ A Coruna Fac Comp Sci Dept Math Res Grp MODES Campus Elvina S-N La Coruna 15071 Spain Westat Corp 1600 Res Blvd Rockville MD 20850 USA Univ Santiago de Compostela Fac Math Dept Stat Math Anal & Optimizat Res Grp MODESTYA Rua Lope Gomez de Marzoa S-N Santiago De Compostela 15782 Spain

The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on aL2-distance comparing a parametric and nonparametric regression estimators is proposed. Asymptotic properties of the test statistic, both under the null hypothesis and under local alternatives, are derived. Additionally, a bootstrap procedure is designed to calibrate the test in practice. Finite sample performance of the test is analyzed through a simulation study, and its applicability is illustrated using a real data example.

关键词： Model checking Spatial correlation local linear regression Least squares Bootstrap

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An algorithm for non-parametric estimation in state-space models

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2021年 153卷 107062-107062页

作者： Thi Tuyet Trang Chau Ailliot, Pierre Monbet, Valerie Univ Rennes IRMAR INRIA Rennes France Univ Brest CNRS LMBA UMR 6205 Brest France

State-space models are ubiquitous in the statistical literature since they provide a flexible and interpretable framework for analyzing many time series. In most practical applications, the state-space model is specified through a parametric model. However, the specification of such a parametric model may require an important modeling effort or may lead to models which are not flexible enough to reproduce all the complexity of the phenomenon of interest. In such situations, an appealing alternative consists in inferring the state-space model directly from the data using a non-parametric framework. The recent developments of powerful simulation techniques have permitted to improve the statistical inference for parametric state-space models. It is proposed to combine two of these techniques, namely the Stochastic Expectation-Maximization (SEM) algorithm and Sequential Monte Carlo (SMC) approaches, for non-parametric estimation in state-space models. The performance of the proposed algorithm is assessed though simulations on toy models and an application to environmental data is discussed. (C) 2020 Elsevier B.V. All rights reserved.

关键词： State-space models Non-parametric statistics SEM algorithm local linear regression Conditional particle filter

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Trend instrumental variable regression with an application to the US New Keynesian Phillips Curve

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ECONOMIC MODELLING 2020年 93卷 595-604页

作者： Chen, Zhihong Xia, Huizhu Univ Int Business & Econ Sch Int Trade & Econ Beijing 100029 Peoples R China Zhejiang Agr & Forestry Univ Dept Stat Hangzhou 311300 Peoples R China

When a linear model suffers from endogeneity, a conventional solution is to use external instrumental variables. Sometimes, however, there are either no suitable external IVs or they are of poor quality. This paper constructs an internal instrumental variable from the time trend in the endogenous regressor without using any external IVs. We show that under some mild conditions this new trend IV estimator has desirable asymptotic properties, and also provide a robust Durbin-Wu-Hausman specification test to demonstrate the necessity of the IV method. Monte Carlo simulations show that the estimator and the test have good finite sample performance. In the end, we apply the trend IV estimator to the US New Keynesian Phillips Curve and find that it works as well as the usual external IVs in the literature.

关键词： Durbin-Wu-Hausman test Endogeneity Instrumental variable local linear regression local trends New keynesian phillips curve

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Policy discontinuity and duration outcomes

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QUANTITATIVE ECONOMICS 2020年第3期11卷 871-916页

作者： van den Berg, Gerard J. Bozio, Antoine Dias, Monica Costa Univ Bristol Dept Econ Bristol Avon England Univ Groningen Groningen Netherlands IFAU Uppsala IZA ZEW Uppsala Sweden IFAU Uppsala IZA CEPR Uppsala Sweden Paris Sch Econ Paris France Inst Fiscal Studies Paris France Univ Porto Inst Fiscal Studies Dept Econ CEPR Porto Portugal IZA Porto Portugal

Causal effects of a policy change on hazard rates of a duration outcome variable are not identified from a comparison of spells before and after the policy change if there is unobserved heterogeneity in the effects and no model structure is imposed. We develop a discontinuity approach that overcomes this by considering spells that include the moment of the policy change and by exploiting variation in the moment at which different cohorts are exposed to the policy change. We prove identification of average treatment effects on hazard rates without model structure. We estimate these effects by kernel hazard regression. We use the introduction of the NDYP program for young unemployed individuals in the UK to estimate average program participation effects on the exit rate to work as well as anticipation effects.

关键词： Policy evaluation hazard rate identification causality regression discontinuity selectivity kernel hazard estimation local linear regression average treatment effect job search assistance youth unemployment C14 C25 J64

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Weighted composite quantile regression for single index model with missing covariates at random

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COMPUTATIONAL STATISTICS 2019年第4期34卷 1711-1740页

作者： Liu, Huilan Yang, Hu Peng, Changgen Guizhou Univ Guizhou Prov Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China Guizhou Univ Coll Math & Stat Guiyang 550025 Guizhou Peoples R China Chongqing Univ Coll Math & Stat Chongqing 401331 Peoples R China

This paper considers weighted composite quantile estimation of the single-index model with missing covariates at random. Under some regularity conditions, we establish the large sample properties of the estimated index parameters and link function. The large sample properties of the parametric part show that the estimator with estimated selection probability have a smaller limiting variance than the one with the true selection probability. However, the large sample properties of the estimated link function indicate that whether weights were estimated or not has no effect on the asymptotic variance. Studies of simulation and the real data analysis are presented to illustrate the behavior of the proposed estimators.

关键词： Horvitz-Thompson property local linear regression Missing at random

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Efficient semiparametric regression for longitudinal data with regularised estimation of error covariance function

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JOURNAL OF NONPARAMETRIC STATISTICS 2019年第4期31卷 867-886页

作者： Jia, Shengji Zhang, Chunming Wu, Hulin Univ Wisconsin Dept Stat Madison WI 53706 USA Yunnan Univ Sch Math & Stat Kunming 650091 Yunnan Peoples R China Univ Texas Hlth Sci Ctr Houston Sch Publ Hlth Dept Biostat Houston TX 77030 USA

Improving estimation efficiency for regression coefficients is an important issue in the analysis of longitudinal data, which involves estimating the covariance matrix of errors. But challenges arise in estimating the covariance matrix of longitudinal data collected at irregular or unbalanced time points. In this paper, we develop a regularisation method for estimating the covariance function and a stepwise procedure for estimating the parametric components efficiently in the varying-coefficient partially linear model. This procedure is also applicable to the varying-coefficient temporal mixed-effects model. Our method utilises the structure of the covariance function and thus has faster rates of convergence in estimating the covariance functions and outperforms the existing approaches in simulation studies. This procedure is easy to implement and its numerical performance is investigated using both simulated and real data.

关键词： Covariance function local linear regression method of regularisation profile weighted least squares semiparametric varying-coefficient partially linear model Sobolov space tensor product space

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