scholarly journals Modeling Post-Liberalized European Gas Market Concentration—A Game Theory Perspective

Forecasting ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Hassan Hamie ◽  
Anis Hoayek ◽  
Hans Auer
Keyword(s):  
Game Theory ◽  
Statistical Tests ◽  
Parametric Method ◽  
Linear Complementarity Problems ◽  
Market Concentration ◽  
Cournot Equilibrium ◽  
Gas Markets ◽  
Gas Market ◽  
Equilibrium Test ◽  
Non Parametric

The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium problems, more precisely non-linear complementarity problems, were used to model European gas markets and their effect on prices. In this research, the parametric and nonparametric game theory methods are employed to study the effect of the market concentration on gas prices. The parametric method takes into account the classical Cournot equilibrium test, with assumptions on cost and demand functions. However, the non-parametric method does not make any prior assumptions, a factor that allows greater freedom in modeling. The results of the parametric method demonstrate that the gas suppliers’ behavior in Austria and The Netherlands gas markets follows the Nash–Cournot equilibrium, where companies act rationally to maximize their payoffs. The non-parametric approach validates the fact that suppliers in both markets follow the same behavior even though one market is more liquid than the other. Interestingly, our findings also suggest that some of the gas suppliers maximize their ‘utility function’ not by only relying on profit, but also on some type of non-profit objective, and possibly collusive behavior.

scholarly journals Unearthing New Genomic Markers of Drug Response by Improved Measurement of Discriminative Power

2015 ◽  
Author(s):  
Cuong C. Dang ◽  
Antonio Peón ◽  
Pedro J. Ballester
Keyword(s):  
Drug Response ◽  
Drug Sensitivity ◽  
Statistical Tests ◽  
Parametric Method ◽  
False Negative ◽  
Discriminative Power ◽  
True Negative ◽  
Genomic Markers ◽  
Non Parametric

AbstractBackgroundOncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs in order to improve patient survival, reduce healthcare costs and enhance success rates in clinical trials. Screening these drugs against a large panel of cancer cell lines has been employed to discover new genomic markers of in vitro drug response, which can now be further evaluated on more accurate tumour models. However, while the identification of discriminative markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.ResultsHere we present a new non-parametric method to measuring the discriminative power of a drug-gene association. This is enabled by the identification of an auxiliary threshold posing this task as a binary classification problem. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers. The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.Abbreviation(WT)wild-type(GDSC)Genomics of Drug Sensitivity in Cancer(TP)true positive(TN)true negative(FP)false positive(FN)false negative(MCC)Matthews Correlation Co-efficient.

scholarly journals Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1169
Author(s):  
Juan Bógalo ◽  
Pilar Poncela ◽  
Eva Senra
Keyword(s):  
Business Cycles ◽  
Real Time ◽  
Parametric Method ◽  
Singular Spectrum Analysis ◽  
The State ◽  
Signal Extraction ◽  
Alternative Procedure ◽  
State Of The Economy ◽  
Regular Patterns ◽  
Non Parametric

Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure.

scholarly journals Cost-benefit analysis in selected air trips using a non parametric method

2011 ◽  
Vol 5 (21) ◽  
pp. 8678-8685
Author(s):  
de Souza Lima Vitor ◽  
Carlos C B Soares de Mello Joatilde o ◽  
Angulo Meza Lidia
Keyword(s):  
Cost Benefit Analysis ◽  
Parametric Method ◽  
Cost Benefit ◽  
Benefit Analysis ◽  
Non Parametric

scholarly journals Inferring the three-dimensional distribution of dust in the Galaxy with a non-parametric method

2017 ◽  
Vol 598 ◽  
pp. A125 ◽  
Author(s):  
S. Rezaei Kh. ◽  
C. A. L. Bailer-Jones ◽  
R. J. Hanson ◽  
M. Fouesneau
Keyword(s):  
Parametric Method ◽  
Three Dimensional ◽  
The Galaxy ◽  
Dimensional Distribution ◽  
Non Parametric

scholarly journals Build a Better Bootstrap and the RAWR Shall Beat a Random Path to Your Door: Phylogenetic Support Estimation Revisited

2020 ◽  
Author(s):  
Wei Wang ◽  
Kevin J. Liu
Keyword(s):  
State Of The Art ◽  
Bootstrap Method ◽  
Parametric Method ◽  
Open Data ◽  
Bootstrap Support ◽  
Future Research ◽  
Evolutionary Divergence ◽  
Type I ◽  
Support Estimation ◽  
Non Parametric

AbstractMotivationThe standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate “phylogenetic support”). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted. Special-purpose fully parametric or semi-parametric methods for phylogenetic support estimation have since been introduced, some of which are intended to address this concern.ResultsIn this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR (“RAndom Walk Resampling”). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the “mirrored inputs” idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR’s performance is compared to the state of the art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support as well as GUIDANCE2, a state-of-the-art purpose-built fully parametric method. Additional simulation study experiments help to clarify practical considerations regarding RAWR support estimation. We conclude with thoughts on future research directions and the untapped potential for sequence-aware non-parametric resampling and re-estimation.AvailabilityData and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/[email protected]

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