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nonparametric spatial regression with spatial autoregressive error structure

STATISTICS 2016年第1期50卷 60-75页

作者： Wang, Hongxia Lin, Jinguan Wang, Jinde Southeast Univ Dept Math Nanjing 211189 Jiangsu Peoples R China Nanjing Univ Dept Math Nanjing 210093 Jiangsu Peoples R China

Spatially correlated data are involved in various environmental studies, thus it is necessary to propose spatial estimation methods that capture spatial correlation so as to improve the accuracy of estimation. In this paper, we propose a three-steps estimation procedure for spatial models with spatial autoregressive error structure. The asymptotic normality of the estimator (m) over tilde*(x) shows that it is more efficient than traditional local linear estimator (m) over cap (x), which ignores the spatial correlation. Results from a simulation study also show that m (m) over tilde*(x) has a better finite sample performance than (m) over cap (x).

关键词： spatial model local linear fitting method spatial autoregressive error structure spatialheterogeneity nonparametric method

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Estimating Conditional Average Treatment Effects

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2015年第4期33卷 485-505页

作者： Abrevaya, Jason Hsu, Yu-Chin Lieli, Robert P. Univ Texas Austin Dept Econ Austin TX 78712 USA Acad Sinica Inst Econ Taipei 115 Taiwan Cent European Univ Dept Econ H-1051 Budapest Hungary Magyar Nemzeti Bank H-1850 Budapest Hungary

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE parameter is nonparametrically identified under unconfoundedness and propose inverse probability weighted estimators for it. Under regularity conditions, some of which are standard and some are new in the literature, we show (pointwise) consistency and asymptotic normality of a fully nonparametric and a semiparametric estimator. We apply our methods to estimate the average effect of a first-time mother's smoking during pregnancy on the baby's birth weight as a function of the mother's age. A robust qualitative finding is that the expected effect becomes stronger (more negative) for older mothers.

关键词： Birth weight Inverse probability weighted estimation nonparametric method Treatment effect heterogeneity

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Diagnostic test accuracy in longitudinal study settings: theoretical approaches with use cases from clinical practice

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JOURNAL OF CLINICAL EPIDEMIOLOGY 2024年 169卷 111314-111314页

作者： Boehnke, Julia Zapf, Antonia Weber, Philipp Karch, Andre Ruebsamen, Nicole Univ Munster Inst Epidemiol & Social Med Albert Schweitzer Campus 1 D-48149 Munster Germany Univ Med Ctr Hamburg Eppendorf Dept Med Biometry & Epidemiol Hamburg Germany Univ Augsburg Math Stat & Artificial Intelligence Med Augsburg Germany

Objectives: In this study, we evaluate how to estimate diagnostic test accuracy (DTA) correctly in the presence of longitudinal patient data (ie, repeated test applications per patient). Study Design and Setting: We used a nonparametric approach to estimate the sensitivity and specificity of three tests for different target conditions with varying characteristics (ie, episode length and disease-free intervals between episodes): 1) systemic inflammatory response syndrome ( n = 36), 2) depression ( n = 33), and 3) epilepsy ( n = 30). DTA was estimated on the levels ' time ', ' block ', and ' pa- tient-time ' for each diagnosis, representing different research questions. The estimation was conducted for the time units per minute, per hour, and per day. Results: A comparison of DTA per and across use cases showed variations in the estimates, which resulted from the used level, the time unit, the resulting number of observations per patient, and the diagnosis-specific characteristics. Intra- and inter-use-case comparisons showed that the time-level had the highest DTA, particularly the larger the time unit, and that the patient-time-level approximated 50% sensitivity and specificity. Conclusion: Researchers need to predefine their choices (ie, estimation levels and time units) based on their individual research aims, estimands, and diagnosis-specific characteristics of the target outcomes to make sure that unbiased and clinically relevant measures are communicated. In cases of uncertainty, researchers could report the DTA of the test using more than one estimation level and/or time unit. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http:// ***/licenses/by-nc/4.0/).

关键词： Diagnostic study Diagnostic test accuracy Longitudinal study Data cluster nonparametric method Estimation level

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nonparametric Analysis of Clustered Multivariate Data

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2010年第490期105卷 864-872页

作者： Nevalainen, Jaakko Larocque, Denis Oja, Hannu Porsti, Ilkka Univ Turku Dept Social Res Turku 20014 Finland HEC Montreal Dept Management Sci Quebec City PQ H3T 2A7 Canada Univ Tampere Tampere Sch Publ Hlth Tampere 33014 Finland Univ Tampere Sch Med Tampere 33014 Finland

There is wide interest in extending univariate and multivariate nonparametric procedures to clustered and hierarchical data. Traditionally, parametric mixed models have been used to account for the correlation structures among the dependent observational units. In this work we extend multivariate nonparametric procedures for one-sample and several-sample location problems to clustered data settings. The results are given for a general score function, but with an emphasis on spatial sign and rank methods. Mixed models notation involving design matrices for fixed and random effects is used throughout. The asymptotic variance formulas and limiting distributions of the test statistics under the null hypothesis and under a sequence of alternatives are derived, as are the limiting distributions for the corresponding estimates. The approach based on a general score function also shows, for example, how M-estimates behave with clustered data. Efficiency studies demonstrate the practical advantages and disadvantages of the use of spatial sign and rank scores, as well as their weighted versions. Small-sample procedures based on sign change and permutation principles are discussed. Further development of nonparametric methods for cluster-correlated data would benefit from the notation already familiar to statisticians working under normality assumptions. Supplemental materials for the article are available online.

关键词： Clustered data Mixed model Multivariate analysis nonparametric method

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The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 129卷

作者： Pan, Xin Wang, Zhanchuan Liu, Suyi Yang, Zi Guluzade, Rufat Liu, Yuanbo Yuan, Jie Yang, Yingbao Hohai Univ Coll Geog & Remote Sensing Nanjing 210098 Peoples R China Hohai Univ Jiangsu Prov Engn Res Ctr Water Resources & Enviro Nanjing 211100 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China Chinese Acad Sci Key Lab Watershed Geog Sci Nanjing Inst Geog & Limnol Nanjing 210008 Peoples R China

Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases on ET estimation, the monthly ET under clear-sky and all-sky conditions was estimated using a nonparametric method at several sites with different humid conditions and vegetation coverage in 2014. 35.7 % of the clear-sky LST values were acquired in the arid region (Heihe River Basin) with different vegetation coverage. As the climate became more humid, only 14.2 % of the clear-sky LST data were available (Poyang Lake Basin). The clear-sky biases of LST variation at the sites resulted in a significant reduction in the accuracy of monthly ET, with an increase in the relative error (RE) of approximately 16.6 %. The impact of clouds can be reduced by at most half by the introduction of all-sky LST products, with a significant decline (6.3 % similar to 34.2 % of ET) in the error contribution of LST to monthly ET. The variation in vegetation coverage of land cover and the change in humidity of the climate significantly affected the influence of the clear-sky biases of LST on the ET estimation and the error contribution to ET. Replacing all-sky LST with clear-sky LST in areas with less vegetation coverage exacerbates the underestimation of ET estimation and strengthens the error contribution of LST with the decline in RE (error contribution of LST) from -7.7 % (-6.3 %) in densely vegetated areas to -29.7 % (-34.2 %) in non-vegetated areas, especially in the summer. Similarly, clear-sky biases of LST tended to have a relatively significant impact on the error contributions of LST to monthly ET estimation when the climate became humid, with a relatively significant enlarging fro

关键词： Clear-sky biases Evapotranspiration Land surface temperature Cloud impact nonparametric method

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More efficient local polynomial regression with random-effects panel data models

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ECONOMETRIC REVIEWS 2018年第7期37卷 760-776页

作者： Yang, Ke Univ Hartford Barney Sch Business Dept Econ Hartford CT 06117 USA

We propose a modification on the local polynomial estimation procedure to account for the within-subject correlation presented in panel data. The proposed procedure is rather simple to compute and has a closed-form expression. We study the asymptotic bias and variance of the proposed procedure and show that it outperforms the working independence estimator uniformly up to the first order. Simulation study shows that the gains in efficiency with the proposed method in the presence of within-subject correlation can be significant in small samples. For illustration purposes, the procedure is applied to explore the impact of market concentration on airfare.

关键词： Kernel regression Local polynomial regression nonparametric method Random-effects panel data model Variance reduction

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nonparametric-likelihood inference based on cost-effectively-sampled-data

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JOURNAL OF APPLIED STATISTICS 2011年第4期38卷 769-783页

作者： Vexler, Albert Liu, Shuling Schisterman, Enrique F. SUNY Buffalo Dept Biostat Buffalo NY 14260 USA NICHHD Div Epidemiol Stat & Prevent Res NIH DHHS Rockville MD USA

Costs associated with the evaluation of biomarkers can restrict the number of relevant biological samples to be measured. This common problem has been dealt with extensively in the epidemiologic and biostatistical literature that proposes to apply different cost-efficient procedures, including pooling and random sampling strategies. The pooling design has been widely addressed as a very efficient sampling method under certain parametric assumptions regarding data distribution. When cost is not a main factor in the evaluation of biomarkers but measurement is subject to a limit of detection, a common instrument limitation on the measurement process, the pooling design can partially overcome this instrumental limitation. In certain situations, the pooling design can provide data that is less informative than a simple random sample;however this is not always the case. Pooled-data-based nonparametric inferences have not been well addressed in the literature. In this article, a distribution-free method based on the empirical likelihood technique is proposed to substitute the traditional parametric-likelihood approach, providing the true coverage, confidence interval estimation and powerful tests based on data obtained after the cost-efficient designs. We also consider several nonparametric tests to compare with the proposed procedure. We examine the proposed methodology via a broad Monte Carlo study and a real data example.

关键词： confidence interval cost-efficient design empirical likelihood nonparametric method pooling design power random sampling Student's t-test type-I error

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Investigating the competitive assumption of Multinomial Logit models of brand choice by nonparametric modeling

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COMPUTATIONAL STATISTICS 2004年第4期19卷 635-657页

作者： Abe, M Boztug, Y Hildebrandt, L Univ Tokyo Fac Econ Bunkyo Ku Tokyo 1130033 Japan Humboldt Univ Inst Mkg D-10178 Berlin Germany

The Multinomial Logit (MNL) model is still the only viable option to study nonlinear responsiveness of utility to covariates nonparametrically. This research investigates whether MNL structure of inter-brand competition is a reasonable assumption, so that when the utility function is estimated nonparametrically, the IIA assumption does not bias the result. For this purpose, the authors compare the performance of two comparable nonparametric choice models that differ in one aspect: one assumes MNL competitive structure and the other infers the pattern of brands' competition nonparametrically from data.

关键词： nonparametric method generalized additive models brand choice IIA Multinomial Logit model scanner panel data

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Seemingly unrelated regression tree

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JOURNAL OF APPLIED STATISTICS 2019年第7期46卷 1177-1195页

作者： Kim, Jaeoh Cho, HyungJun Korea Univ Dept Stat Seoul 136701 South Korea

nonparametric seemingly unrelated regression provides a powerful alternative to parametric seemingly unrelated regression for relaxing the linearity assumption. The existing methods are limited, particularly with sharp changes in the relationship between the predictor variables and the corresponding response variable. We propose a new nonparametric method for seemingly unrelated regression, which adopts a tree-structured regression framework, has satisfiable prediction accuracy and interpretability, no restriction on the inclusion of categorical variables, and is less vulnerable to the curse of dimensionality. Moreover, an important feature is constructing a unified tree-structured model for multivariate data, even though the predictor variables corresponding to the response variable are entirely different. This unified model can offer revelatory insights such as underlying economic meaning. We propose the key factors of tree-structured regression, which are an impurity function detecting complex nonlinear relationships between the predictor variables and the response variable, split rule selection with negligible selection bias, and tree size determination solving underfitting and overfitting problems. We demonstrate our proposed method using simulated data and illustrate it using data from the Korea stock exchange sector indices.

关键词： Regression tree seemingly unrelated regression selection bias nonparametric method

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Migration and technical efficiency in cereal production: evidence from Burkina Faso

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AGRICULTURAL ECONOMICS 2010年第5期41卷 385-395页

作者： Wouterse, Fleur IFPRI Dakar Immeuble Ousseynou Thiam Gueye Dakar Senegal

This article uses a double bootstrap procedure and survey data from Burkina Faso in a two-stage estimation to explore ways in which continental and intercontinental migration determine efficiency in cereal production of rural households. Findings suggest that continental migration has a positive relation and intercontinental migration no relation with technical efficiency. For continental migrant households, migration has removed surplus male labor, a cause for inefficiency in production. Intercontinental migration leads to a gender imbalance in the household, which cannot be compensated for by investments in farm equipment. The failure of intercontinental migration to transform cereal production from traditional to modern is attributed to an imperfect market environment.

关键词： C14 J22 J61 O13 Q12 West Africa Burkina Faso Rural livelihoods Migration Technical efficiency nonparametric method

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