Leave-one-out procedures for nonparametric error estimates

Fukunaga, Keinosuke; Hummels, D.M.

doi:10.1109/34.19039

Cited by 77 publications

(57 citation statements)

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“…Further, the autocorrelation test results of residual diagnostics based on the Q-statistic are listed in Table 7, showing that there is no autocorrelation in the residual series of the two models, that is, the estimated results of DOLS and FMOLS are valid, and the parameter estimates are consistent. Meanwhile, the robustness of the panel estimated results to the inclusion or exclusion of particular countries are examined using the leave-one-out (LOO) analysis, which is widely used in the field of machine learning algorithms [54][55][56]. In this paper, the idea of LOO analysis can be as follows: on the basis of the original panel set, remove any one country, and the remaining countries constitute a new panel, then re-estimate the new panel and compare the results with the original panel estimation results, so as to analyze the impact of removing a country.…”

Section: Panel Data Model Estimationmentioning

confidence: 99%

Carbon Emission Intensity, Economic Development and Energy Factors in 19 G20 Countries: Empirical Analysis Based on a Heterogeneous Panel from 1990 to 2015

Shi

et al. 2018

Sustainability

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Abstract:The increasing global climate problem caused by excessive carbon emissions results in global carbon emission reduction governance becoming a top priority and requires close international coordination. Group of Twenty (G20) is gradually becoming the leading agency of global carbon emission reduction governance, but the unbalanced development among G20 countries has hindered the full play of G20's role. Thus, this paper aims to examine the interrelationships among economic development mode, economic development level, and energy factors including energy use efficiency and structure in 19 G20 countries over the period 1990-2015. Considering the panel heterogeneity and the endogeneity of variables, a series of heterogeneous panel analysis techniques are employed in this paper. The empirical findings suggest that for the panel, the improvement of energy use efficiency and the optimization of energy use structure can help to achieve a low-carbon development mode, implying that some international agreements such as the Copenhagen Accord and Paris Agreement on Climate Change are necessary, binding, and effective. However, for individuals, energy factors and development level influence development mode differently across countries, revealing that each country should formulate specific policies that are consistent with its own actual situation. Finally, this paper discusses the role that G20 can play in the global carbon emissions reduction governance, which provides a reference for global low-carbon and sustainable development.

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Section: Panel Data Model Estimationmentioning

confidence: 99%

Carbon Emission Intensity, Economic Development and Energy Factors in 19 G20 Countries: Empirical Analysis Based on a Heterogeneous Panel from 1990 to 2015

Shi

et al. 2018

Sustainability

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“…Similarly, LOO evaluates each unknown feature vector, and then produces a basis to evaluate classifier designs for powder classification [7], [28]. Therefore, LOO accuracy is also the percentage of correctly classified data sets.…”

Section: Performance Assessment Of Classificationmentioning

confidence: 99%

“…Cross-validation methods [26], [27] and leave one out (LOO) [28], [29] estimator within the deconvolved T-ray data set are utilized to provide a nearly unbiased estimate of the prediction error rate. The performance of classifying the RNA samples are evaluated using eightfold cross-validation, while the powdered material classification is validated using LOO.…”

Section: Performance Assessment Of Classificationmentioning

confidence: 99%

Support Vector Machine Applications in Terahertz Pulsed Signals Feature Sets

Yin

Fischer

et al. 2007

IEEE Sensors J.

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“…We use the leave-one-out technique [4] (a machine learning evaluation technique) to show the performance of our approach. Leave one out involves hiding one trust edge and then trying to predict it.…”

Section: Planmentioning

confidence: 99%

Inferring Trust Based on Similarity with TILLIT

Tavakolifard

Herrmann

Knapskog

2009

Trust Management III

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Abstract.A network of people having established trust relations and a model for propagation of related trust scores are fundamental building blocks in many of todays most successful e-commerce and recommendation systems. However, the web of trust is often too sparse to predict trust values between non-familiar people with high accuracy. Trust inferences are transitive associations among users in the context of an underlying social network and may provide additional information to alleviate the consequences of the sparsity and possible cold-start problems. Such approaches are helpful, provided that a complete trust path exists between the two users. An alternative approach to the problem is advocated in this paper. Based on collaborative filtering one can exploit the like-mindedness resp. similarity of individuals to infer trust to yet unknown parties which increases the trust relations in the web. For instance, if one knows that with respect to a specific property, two parties are trusted alike by a large number of different trusters, one can assume that they are similar. Thus, if one has a certain degree of trust to the one party, one can safely assume a very similar trustworthiness of the other one. In an attempt to provide high quality recommendations and proper initial trust values even when no complete trust propagation path or user profile exists, we propose TILLIT -a model based on combination of trust inferences and user similarity. The similarity is derived from the structure of the trust graph and users' trust behavior as opposed to other collaborative-filtering based approaches which use ratings of items or user's profile. We describe an algorithm realizing the approach based on a combination of trust inferences and user similarity, and validate the algorithm using a real large-scale data-set.

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Leave-one-out procedures for nonparametric error estimates

Cited by 77 publications

References 1 publication

Carbon Emission Intensity, Economic Development and Energy Factors in 19 G20 Countries: Empirical Analysis Based on a Heterogeneous Panel from 1990 to 2015

Carbon Emission Intensity, Economic Development and Energy Factors in 19 G20 Countries: Empirical Analysis Based on a Heterogeneous Panel from 1990 to 2015

Support Vector Machine Applications in Terahertz Pulsed Signals Feature Sets

Inferring Trust Based on Similarity with TILLIT

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