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Estimation of discrete choice models with error in variables: An application to revealed preference data with aggregate service level variables

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2024年 185卷

作者： Batarce, Marco Univ San Sebastian Fac Econ & Govt Santiago Chile

This article proposes a method to estimate disaggregated discrete choice models with errors in the variables. The objective is to estimate the discrete choice models' coefficients to compute the value of time and use it for cost-benefit analysis in transportation planning. The method is general, as it only requires a validation sample to estimate the conditional density of the error-free variables given the mismeasured variables. More specifically, we assume that the attributes of the chosen alternative are reported without error in revealed preference surveys, and we use this information as the validation sample. The mismeasured variables may be spatially aggregate service levels from mobility surveys or transportation network models. Monte Carlo simulations show that the proposed method substantially reduces bias in parameters. We validate the technique with two real data sets from Santiago, Chile.

关键词： Discrete choice error in variables Logit Time value

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COCOLASSO FOR HIGH-DIMENSIONAL error-IN-variables REGRESSION

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ANNALS OF STATISTICS 2017年第6期45卷 2400-2426页

作者： Datta, Abhirup Zou, Hui Johns Hopkins Univ Dept Biostat 615 N Wolfe St Baltimore MD 21205 USA Univ Minnesota Sch Stat 224 Church St SE Minneapolis MN 55455 USA

Much theoretical and applied work has been devoted to high-dimensional regression with clean data. However, we often face corrupted data in many applications where missing data and measurement errors cannot be ignored. Loh and Wainwright [Ann. Statist. 40 (2012) 1637-1664] proposed a non-convex modification of the Lasso for doing high-dimensional regression with noisy and missing data. It is generally agreed that the virtues of convexity contribute fundamentally the success and popularity of the Lasso. In light of this, we propose a new method named CoCoLasso that is convex and can handle a general class of corrupted datasets. We establish the estimation error bounds of CoCoLasso and its asymptotic sign-consistent selection property. We further elucidate how the standard cross validation techniques can be misleading in presence of measurement error and develop a novel calibrated cross-validation technique by using the basic idea in CoCoLasso. The calibrated cross-validation has its own importance. We demonstrate the superior performance of our method over the nonconvex approach by simulation studies.

关键词： Convex optimization error in variables high-dimensional regression LASSO missing data

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Consistent estimation in measurement error models with near singular covariance

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INDIAN JOURNAL OF PURE & APPLIED MATHEMATICS 2022年第1期53卷 32-48页

作者： Sarma, B. Bhargavarama Shoba, B. Apxor Technol Solut Pvt Ltd IIIT Room 212 CIEVindya C4 Block Hyderabad 500032 Telangana India Univ Hyderabad Sch Math & Stat Hyderabad 500046 Telangana India

Measurement error models are studied extensively in the literature. In these models, when the measurement error variance Sigma(delta delta) is known, the estimating techniques require positive definiteness of the matrix S-xx - Sigma(delta delta), even when this is positive definite, it might be near singular if the number of observations is small. There are alternative estimators discussed in literature when this matrix is not positive definite. In this paper, estimators when the matrix S-xx - Sigma(delta delta) is near singular are proposed and it is shown that these estimators are consistent and have the same asymptotic properties as the earlier ones. In addition, we show that our estimators work far better than the earlier estimators in case of small samples and equally good for large samples.

关键词： Measurement error error in variables Near singular covariance Ridge regression

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Technical efficiency and farmland expansion: Evidence from oil palm smallholders in Indonesia

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AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS 2022年第4期104卷 1364-1387页

作者： Dalheimer, Bernhard Kubitza, Christoph Bruemmer, Bernhard Univ Gottingen Dept Agr Econ & Rural Dev Gottingen Germany German Inst Global & Area Studies GIGA Hamburg Germany Univ Gottingen Ctr Biodivers & Sustainable Land Use Gottingen Germany

This study asks whether innovation in smallholder production reduces or accelerates land expansion. Even though innovation in agriculture has reduced land expansion globally, rebound effects can occur locally and often at the expense of vital ecosystem functions. In contrast to other studies that investigate rebound effects in response to technological innovation, our study focuses on technical efficiency, the remaining component of total factor productivity. We use a short panel dataset from smallholder oil palm farmers in Sumatra, Indonesia, and develop a two-stage approach in which we estimate technical efficiency and determine its land expansion effect. Our findings suggest that technical efficiency and in particular land efficiency are low, indicating that 50% of the currently cultivated land could be spared. However, the land-sparing effect of increasing technical efficiency is at risk of being offset by about half due to a rebound effect. To maximize the conservation potential from increasing smallholder efficiency, policies need to simultaneously incentivize well-functioning land markets and stricter protection measures for land with high ecological value to mitigate local rebound effects.

关键词： error in variables farmland expansion linear mixed models palm oil production rebound effect smallholder production stochastic production frontier technical efficiency

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Derivation And Practical Comparison Of Recursive Ls And Tls System Identification Methods

Derivation And Practical Comparison Of Recursive Ls And Tls ...

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作者： Friml, Dominik Brno University of Technology

The least squares (LS) type of methods are the most widely used methods in system identificationdespite their obvious imperfection. Such methods use a regressor, that is supposed not tocontain any error, notwithstanding that it is constructed from measured data. This can be solved byusing the total least squares (TLS) type of methods. Derivation of both batch and recursive methodsof LS and TLS for identification and their practical comparison is presented in this paper

关键词： error in variables recursive least squares recursive total least squares akaike informationcriteria bayes information criteria identification Rayleigh quotient inverse iteration

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Structure in total least squares parameter estimation for electrical networks

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IFAC-PapersOnLine 2018年第23期51卷 420-425页

作者： Lang, Adair Shames, Iman Cantoni, Michael Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVictoria3010 Australia

A total least squares approach to estimating the line admittances of an electrical network for power distribution is explored. It is observed that the Newton iterates for solving the corresponding non-linear Karush-Kuhn-Tucker (KKT) optimality conditions are structured for path and tree networks. In particular, it is revealed that a condensed form of the linear system of equations to be solved at each Newton step has a sparse block arrow-head structure, which can be exploited to improve the scalability of the approach. A numerical example for a path network is presented. © 2018

关键词： Parameter estimation Electric network parameters Large scale systems Linear systems Electrical networks error in variables Head structure Karush Kuhn Tucker (KKT) Linear system of equations Optimality conditions Power distributions Total least squares

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A kernel-based nonparametric approach to direct data-driven control of LTI systems

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IFAC-PapersOnLine 2018年第15期51卷 1026-1031页

作者： Cerone, V. Regruto, D. Abuabiah, M. Fadda, E. Dipartimento di Automatica e Informatica Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy

In this work we propose a kernel-based robust nonparametric approach to direct data-driven control of linear systems, in the presence of bounded noise affecting the measurements data. First we formulate the problem of designing a controller in order to match the behavior of a given reference model. Then, we design the controller by applying previous results by some of the authors in the field of kernel-based nonparametric error-in-variables identification. Finally, we show the effectiveness of the presented technique by means of two simulation examples. © 2018

关键词： Linear control systems Controllers Iterative methods Linear systems Bounded noise Data driven control error in variables Non parametric Nonparametric approaches Reference modeling Simulation example

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A kernel-based nonparametric approach to direct data-driven control of LTI systems

A kernel-based nonparametric approach to direct data-driven ...

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18th IFAC Symposium on System Identification (SYSID)

作者： Cerone, V. Regruto, D. Abuabiah, M. Fadda, E. Politecn Torino Dipartimento Automat & Informat Corso Duca Abruzzi 24 I-10129 Turin Italy

关键词： kernel based nonparametric error in variables Non-iterative Direct Data Driven Control tuning

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Model agnostic time series analysis via matrix estimation

Model agnostic time series analysis via matrix estimation

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Proceedings of the ACM on Measurement and Analysis of Computing Systems

作者： Agarwal, Anish Amjad, Muhammad Jehangir Shah, Devavrat Shen, Dennis Massachusetts Institute of Technology 32-D666 Vassar St CambridgeMA02139 United States Massachusetts Institute of Technology 32-D560 Vassar St. CambridgeMA02139 United States Massachusetts Institute of Technology 32-D670 Vassar St CambridgeMA02139 United States

We propose an algorithm to impute and forecast a time series by transforming the observed time series into a matrix, utilizing matrix estimation to recover missing values and de-noise observed entries, and performing linear regression to make predictions. At the core of our analysis is a representation result, which states that for a large class of models, the transformed time series matrix is (approximately) low-rank. In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in the time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation. The key to establishing this link is constructing a Page matrix with non-overlapping entries rather than a Hankel matrix as is commonly done in the literature (e.g., SSA). This particular matrix structure allows us to provide finite sample analysis for imputation and prediction, and prove the asymptotic consistency of our method. Another salient feature of our algorithm is that it is model agnostic with respect to both the underlying time dynamics and the noise distribution in the observations. The noise agnostic property of our approach allows us to recover the latent states when only given access to noisy and partial observations a la a Hidden Markov Model; e.g., recovering the time-varying parameter of a Poisson process without knowing that the underlying process is Poisson. Furthermore, since our forecasting algorithm requires regression with noisy features, our approach suggests a matrix estimation based method-coupled with a novel, non-standard matrix estimation error metric-to solve the error-in-variable regression problem, which could be of interest in its own right. Through synthetic and real-world datasets, we demonstrate that our algorithm outperforms standard software packages (including R libraries) in the presence of missing data as well as high levels of noise. © 2018 Copyright held by the owner/author(s).

关键词： Time series analysis Forecasting Harmonic analysis Hidden Markov models Matrix algebra Recovery Regression analysis Sampling Spectrum analysis Asymptotic consistencies error in variables Forecasting algorithm Partial observation Particular matrixes Real world datasets Singular spectrum analysis Time varying parameter

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Estimation in autoregressive models with surrogate data and validation data

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第3期46卷 1532-1545页

作者： Yu, Shi-Hang Wang, De-Hui Li, Kun Zhao, Zhi-Wen Qiqihar Univ Dept Maths Qiqihar Peoples R China Jilin Univ Coll Math Changchun 130012 Peoples R China Jilin Normal Univ Coll Math Siping Peoples R China

Time-series data are often subject to measurement error, usually the result of needing to estimate the variable of interest. Generally, however, the relationship between the surrogate variables and the true variables can be rather complicated compared to the classical additive error structure usually assumed. In this article, we address the estimation of the parameters in autoregressive models in the presence of function measurement errors. We first develop a parameter estimation method with the help of validation data;this estimation method does not depend on functional form and the distribution of the measurement error. The proposed estimator is proved to be consistent. Moreover, the asymptotic representation and the asymptotic normality of the estimator are also derived, respectively. Simulation results indicate that the proposed method works well for practical situation.

关键词： Asymptotic normality Autoregression error in variables Validation data 62F12 62F10

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