Strategy-proof estimators for simple regression

Perote, Javier; Perote‐Peña, Juan

doi:10.1016/s0165-4896(03)00085-4

Cited by 41 publications

(52 citation statements)

References 6 publications

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“…Our second hypothesis states that the biases of the RL estimator should be lower than those of the OLS estimator (Hypothesis 2.A), and that more information leads to increase biases for the OLS estimator in Experiment 1 and for the RL estimator in Experiment 2 (Hypothesis 2.B). Note that, theoretically, under the possibility of strategic data manipulation OLS is an inconsistent estimator but RL is still consistent (see Perote and Perote-Peña 2004). Table 2 evaluates these hypotheses by presenting GLS panel regressions on the bias of the slope and intercept, respectively.…”

Section: Biasednessmentioning

confidence: 99%

“…2 These estimators can be obtained by using the properties of the median that has been shown to be strategy-proof when a public good has to be allocated on the real line and when individuals have single-peaked preferences on this domain (see, Moulin 1980). The extension of this "median voter" theorem to the twodimensional context together with a whole family of strategy-proof estimators called "clockwise repeated median estimators" (CRM hereafter) can be found in Perote and Perote-Peña (2004). We will introduce these estimators formally in the next section.…”

Section: Introductionmentioning

confidence: 99%

“…Experiment While the theoretical results in Perote and Perote-Peña (2004) reveal that the class of CRM estimators outperforms the OLS estimator in terms of its manipulability, it is still an open question whether individuals take the incentives adequately into account. To study this, we run a laboratory experiment that is organized as follows: Each subject i = 1, 2, .…”

Section: Introductionmentioning

confidence: 99%

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Strategic behavior in regressions: an experimental study

2014

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We study experimentally in the laboratory the situation when individuals have to report their private information about a (dependent) variable to a public authority that then makes inference about the true values given a known (independent) variable using a regression technique. It is assumed that individuals prefer this predicted value to be as close as possible to their true value (single-peaked preferences). Consistent with the theoretical literature, we show that subjects misrepresent their private information more when an ordinary least squares (OLS) regression is implemented than when the so-called resistant line (RL) estimator is employed. The latter extends the median voter theorem to the two-dimensional setting and belongs to the family of robust estimation techniques. In fact, we find that OLS involves serious biases but the RL estimation is empirically unbiased. Furthermore, subjects never earn less when the RL is applied.

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Section: Biasednessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Strategic behavior in regressions: an experimental study

2014

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“…Most closely related to our work is that of Perote and Perote-Peña [8]. The authors essentially study the setting where each agent controls one point of the input space, in a framework that is not learning-theoretic.…”

Section: Related Workmentioning

confidence: 99%

“…A concrete economic motivation for this setting is given by Perote and Perote-Peña [8]. The authors consider a monopolist trade union in some sector that has to set a common hourly wage for its members.…”

Section: Degenerate Distributionsmentioning

confidence: 99%

Incentive compatible regression learning

Dekel¹,

Fischer²,

Procaccia³

2010

Journal of Computer and System Sciences

127

151

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We initiate the study of incentives in a general machine learning framework. We focus on a game-theoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space. This conflict potentially gives rise to untruthfulness on the part of the agents. In the restricted but important case when every agent cares about a single point, and under mild assumptions, we show that agents are motivated to tell the truth. In a more general setting, we study the power and limitations of mechanisms without payments. We finally establish that, in the general setting, the VCG mechanism goes a long way in guaranteeing truthfulness and economic efficiency.

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Detecting systematic anomalies affecting systems when inputs are stationary time series

Sun

Yang

Zitikis

2022

Appl Stoch Models Bus & Ind

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We develop an anomaly detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly free inputs (i.e., those before contamination) to originate from a wide class of random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally nonstationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly free inputs following an ARMA time series model under various contamination scenarios.

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Strategy-proof estimators for simple regression

Cited by 41 publications

References 6 publications

Strategic behavior in regressions: an experimental study

Strategic behavior in regressions: an experimental study

Incentive compatible regression learning

Detecting systematic anomalies affecting systems when inputs are stationary time series

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