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The COVID-19 pandemic and house price convergence in Poland

JOURNAL OF HOUSING AND THE BUILT ENVIRONMENT 2025年第2期40卷 577-602页

作者： Tomal, Mateusz Cracow Univ Econ Dept Real Estate & Investment Econ Rakowicka 27 PL-31510 Krakow Poland

The effect of the COVID-19 pandemic on price convergence in the housing market is largely unknown. This paper aims to assess the impact of the pandemic on the convergence of sales and rental housing prices. The residential markets in Polish voivodeship capitals were chosen as a case study. The convergence evaluation was based on a local linear version of the log t regression test, which allowed the estimation of a time-varying convergence speed parameter. The causal effect of the pandemic on convergence was examined using a Bayesian structural time series model. The study results revealed that the pandemic led to divergence in the primary sales housing market, growth convergence in the secondary sales housing market, and a weakening growth convergence in the rental housing market. Finally, this paper provides the R function logt that enables running the local linear and dummy variable log t regression tests.

关键词： Convergence local linear regression Log t test COVID-19

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Kernel regression analysis of tie-breaker designs

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ELECTRONIC JOURNAL OF STATISTICS 2023年第1期17卷 243-290页

作者： Kluger, Dan M. Owen, Art B. Stanford Univ Dept Stat Stanford CA 94305 USA

Tie-breaker experimental designs are hybrids of Randomized in which subjects with moderate scores are placed in an RCT while subjects with extreme scores are deterministically assigned to the treatment or control group. In settings where it is unfair or uneconomical to deny the treatment to the more deserving recipients, the tie-breaker design (TBD) trades off the practical advantages of the RDD with the statistical advantages of the RCT. The practical costs of the randomization in TBDs can be hard to quantify in generality, while the statistical benefits conferred by randomization in TBDs have only been studied under linear and quadratic models. In this paper, we discuss and quantify the statistical benefits of TBDs without using parametric modelling assumptions. If the goal is estimation of the average treatment effect or the treatment effect at more than one score value, the statistical benefits of using a TBD over an RDD are apparent. If the goal is nonparametric estimation of the mean treatment effect at merely one score value, we prove that about 2.8 times more subjects are needed for an RDD in order to achieve the same asymptotic mean squared error. We further demonstrate using both theoretical results and simulations from the Angrist and Lavy (1999) classroom size dataset, that larger experimental radii choices for the TBD lead to greater statistical efficiency.

关键词： Causal inference experimental design hybrid experiments local linear regression regression discontinuity designs

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Partially linear Single-Index Model in the Presence of Measurement Error

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Journal of Systems Science & Complexity 2022年第6期35卷 2361-2380页

作者： LIN Hongmei SHI Jianhong TONG Tiejun ZHANG Riquan School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of EducationEast China Normal UniversityShanghai 200062China School of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China Department of Mathematics Hong Kong Baptist UniversityHongKong 519087China School of Statistics East China Normal UniversityShanghai 200062China

The partially linear single-index model(PLSIM) is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates. This paper considers the PLSIM with measurement error possibly in all the variables. The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function. The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method, and apply it to analyze the osteoporosis prevention data.

关键词： local linear regression measurement error partially linear model SIMEX single-index model

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Comparison Between Empirical Mode Decomposition and local linear Quantile regression in the Presence of Boundaries

Comparison Between Empirical Mode Decomposition and Local Li...

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21st National Symposium on Mathematical Sciences (SKSM)

作者： Jaber, Abobaker M. Ismail, Mohd Tahir Univ Sains Malaysia Sch Math Sci Usm 11800 Penang Malaysia

ISBN: (纸本)9780735412415

Quantile regression has been explored extensively by many researchers in the last two decades. This branch of regression provides a thorough and satisfactory approach to the analysis of regression models. Nonparametric quantile regressions can sometimes produce much valuable information concerning departures from standard model assumptions (like nonlinearity and heteroscedasicity). However, a novel method known as empirical mode decomposition (EMD) is capable of providing a good description of the non-stationary and nonlinear relationships among random variables. This study compares nonparametric quantile regressions (local linear quantile regressions) and EMD in the presence of boundaries. A simulation study is conducted to assess the numerical result.

关键词： End effect empirical mode decomposition local linear regression

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Estimation in single-index varying-coefficient panel data model

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2022年第12期51卷 3864-3885页

作者： Wang, Tonghui Wang, Liming Zhou, Xian Shanghai Univ Finance & Econ Sch Stat & Management 777 Guoding Rd Shanghai 200433 Peoples R China Macquarie Univ Dept Appl Finance & Actuarial Studies N Ryde NSW Australia

In this paper, we study the single-index varying-coefficient panel data model. Combining the refined minimum average variance estimation (RMAVE) method with the local linear regression, we estimate the parameters in single index and link function, and explain the steps of the iterative algorithm. Under certain regularity conditions, the asymptotic properties of the estimators of the parameters and link functions are derived. Finally, numerical simulations are presented and our model is shown to perform better than the single-index panel data model in a real-data example.

关键词： Single-index varying-coefficient model panel data minimum average variance estimation local linear regression

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Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty

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MATHEMATICS OF OPERATIONS RESEARCH 2022年第2期47卷 1681-1705页

作者： Liu, Junyi Li, Guangyu Sen, Suvrajeet Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90089 USA Univ Southern Calif Dept Ind & Syst Engn Los Angeles CA 90089 USA

Predictive analytics, empowered by machine learning, is usually followed by decision-making problems in prescriptive analytics. We extend the previous sequential prediction-optimization paradigm to a coupled scheme such that the prediction model can guide the decision problem to produce coordinated decisions yielding higher levels of performance. Specifically, for stochastic programming (SP) models with latently decision-dependent uncertainty, without any parametric assumption of the latent dependency, we develop a coupled learning enabled optimization (CLEO) algorithm in which the learning step of predicting the local dependency and the optimization step of computing a candidate decision are conducted interactively. The CLEO algorithm automatically balances the exploration and exploitation via the trust region method with active sampling. Under certain assumptions, we show that the sequence of solutions provided by CLEO converges to a directional stationary point of the original nonconvex and nonsmooth SP problem with probability 1. In addition, we present preliminary experimental results which demonstrate the computational potential of this data-driven approach.

关键词： stochastic programming endogenous uncertainty trust region method local linear regression

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Building local Models for Flexible Degradation Modeling and Prognostics

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第4期19卷 3483-3495页

作者： Song, Changyue Zheng, Ziqian Liu, Kaibo Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

To avoid unexpected failures of engineering systems, sensors have been widely used to monitor the degradation process of the systems. A number of studies have been conducted to analyze the collected sensor signals and predict the failure time. However, the existing studies are usually restricted and cannot be adapted to different practical situations. In this paper, we propose a systematic method for degradation modeling and prognosis that can be widely applied in different scenarios. In particular, the proposed method is capable to handle one or multiple sensors, powerful to capture the nonlinear relations between sensor signals and the degradation process with few assumptions, generic to consider multiple failure modes, flexible to deal with unequally spaced sensor measurements or asynchronous signals, and easily understandable with little preprocessing required. The main idea is to predict the failure time of an in-service unit based on a subset of the nearest historical units, where features are extracted from each sensor to describe the progression of sensor signals and local linear regression models are constructed to establish the relation between failure time and the extracted features. The prediction variance is then used as the goodness-of-fit measure, based on which decision-level fusion and feature-level fusion are proposed to combine multiple sensors. A case study with two datasets on the degradation modeling of aircraft engines is conducted which shows satisfactory performance of the proposed method.

关键词： Sensors Degradation Feature extraction Sensor phenomena and characterization Sensor fusion Modeling Prognostics and health management Asynchronous signals data fusion failure time prediction local linear regression multiple failure modes

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Goodness-of-fit tests for multiple regression with circular response

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2022年第9期92卷 1941-1963页

作者： Meilan-Vila, A. Francisco-Fernandez, M. Crujeiras, R. M. Univ Carlos III Madrid Dept Stat Ave Univ 30 Madrid 28911 Spain Univ A Coruna Fac Comp Sci Dept Math Res Grp MODESCITIC Campus Elvina S-N La Coruna 15071 Spain Univ Santiago de Compostela Fac Math Dept Stat Math Anal & Optimizat Santiago De Compostela Spain

Testing procedures for assessing a parametric regression model with a circular response and an R-d-valued covariate are proposed and analysed in this work. The test statistics are based on a circular distance comparing a (non-smoothed or smoothed) parametric circular regression estimator and a nonparametric one. Two bootstrap procedures for calibrating the tests in practice are also presented. Finite sample performance of the tests in different scenarios is analysed by simulations and illustrated with real data examples.

关键词： Model checking circular data local linear regression bootstrap

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Direct local linear estimation for Sharpe ratio function

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CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2022年第1期50卷 36-58页

作者： Lin, Hongmei Tong, Tiejun Wang, Yuedong Xu, Wenchao Zhang, Riquan Shanghai Univ Int Business & Econ Sch Stat & Informat Shanghai Peoples R China East China Normal Univ Minist Educ Key Lab Adv Theory & Applicat Stat & Data Sci Shanghai Peoples R China Hong Kong Baptist Univ Dept Math Hong Kong Peoples R China Univ Calif Santa Barbara Dept Stat & Appl Probabil Santa Barbara CA 93106 USA Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China East China Normal Univ Sch Stat Shanghai Peoples R China

Nonparametric regression has been widely used to deal with nonlinearity and heteroscedasticity in financial time series. As the ratio of the mean and standard deviation functions, the Sharpe ratio function is one of the most commonly used risk/return measures in financial econometrics. Most existing methods take an indirect procedure, which first estimates the mean and variance functions and then applies these two functions to estimate the Sharpe ratio function. In practice, however, such an indirect procedure can often be less efficient. In this article, we propose a direct method to estimate the Sharpe ratio function by local linear regression. We further establish the asymptotic normality of the proposed estimator, apply Monte Carlo simulations to evaluate its finite sample performance, and compare it with the indirect method. The usefulness of our new method is also illustrated through a real data analysis.

关键词： Heteroscedasticity local likelihood estimation local linear regression nonparametric regression Sharpe ratio function

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Estimation and inference for the mediation effect in a time-varying mediation model

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BMC MEDICAL RESEARCH METHODOLOGY 2022年第1期22卷 1-12页

作者： Cai, Xizhen Coffman, Donna L. Piper, Megan E. Li, Runze Williams Coll Dept Math & Stat Williamstown MA 01267 USA Temple Univ Dept Epidemiol & Biostat Philadelphia PA 19122 USA Univ Wisconsin Sch Med & Publ Hlth Ctr Tobacco Res & Intervent Madison WI USA Univ Wisconsin Dept Med Sch Med & Publ Hlth Madison WI USA Penn State Univ Dept Stat University Pk PA 16802 USA

Background Traditional mediation analysis typically examines the relations among an intervention, a time-invariant mediator, and a time-invariant outcome variable. Although there may be a total effect of the intervention on the outcome, there is a need to understand the process by which the intervention affects the outcome (i.e., the indirect effect through the mediator). This indirect effect is frequently assumed to be time-invariant. With improvements in data collection technology, it is possible to obtain repeated assessments over time resulting in intensive longitudinal data. This calls for an extension of traditional mediation analysis to incorporate time-varying variables as well as time-varying effects. Methods We focus on estimation and inference for the time-varying mediation model, which allows mediation effects to vary as a function of time. We propose a two-step approach to estimate the time-varying mediation effect. Moreover, we use a simulation-based approach to derive the corresponding point-wise confidence band for the time-varying mediation effect. Results Simulation studies show that the proposed procedures perform well when comparing the confidence band and the true underlying model. We further apply the proposed model and the statistical inference procedure to data collected from a smoking cessation study. Conclusions We present a model for estimating time-varying mediation effects that allows both time-varying outcomes and mediators. Simulation-based inference is also proposed and implemented in a user-friendly R package.

关键词： Ecological momentary assessment Intensive longitudinal data local linear regression Nonparametric regression Varying coefficient model

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