The Experts below are selected from a list of 101433 Experts worldwide ranked by ideXlab platform
Edwin L Turner - One of the best experts on this subject based on the ideXlab platform.
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a Maximum Likelihood Method to improve faint source flux and color estimates
Publications of the Astronomical Society of the Pacific, 1998Co-Authors: David W Hogg, Edwin L TurnerAbstract:Flux estimates for faint sources or transients are systematically biased high because there are far more truly faint sources than bright. Corrections that account for this effect are presented as a function of signal- to-noise ratio and the (true) slope of the faint-source number-flux relation. The corrections depend on the source being originally identified in the image in which it is being photometered. If a source has been identified in other data, the corrections are different; a prescription for calculating the corrections is presented. Implications of these corrections for analyses of surveys are discussed; the most important is that sources identified at signal-to-noise ratios of 4 or less are practically useless.
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a Maximum Likelihood Method for improving faint source flux and color estimates
arXiv: Astrophysics, 1997Co-Authors: David W Hogg, Edwin L TurnerAbstract:Flux estimates for faint sources or transients are systematically biased high because there are far more truly faint sources than bright. Corrections which account for this effect are presented as a function of signal-to-noise ratio and the (true) slope of the faint-source number-flux relation. The corrections depend on the source being originally identified in the image in which it is being photometered. If a source has been identified in other data, the corrections are different; a prescription for calculating the corrections is presented. Implications of these corrections for analyses of surveys are discussed; the most important is that sources identified at signal-to-noise ratios of four or less are practically useless.
Josef Steinebach - One of the best experts on this subject based on the ideXlab platform.
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estimation in random coefficient autoregressive models
Journal of Time Series Analysis, 2006Co-Authors: Lajos Horvath, Josef SteinebachAbstract:We propose the quasi-Maximum Likelihood Method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions. Copyright 2006 Blackwell Publishing Ltd.
Onder Guler - One of the best experts on this subject based on the ideXlab platform.
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a novel energy pattern factor Method for wind speed distribution parameter estimation
Energy Conversion and Management, 2015Co-Authors: Seyit Ahmet Akdag, Onder GulerAbstract:Abstract Power output of wind turbine depends on many factors. Among them, the most crucial one is wind speed. Since wind speed data is a significant factor for wind energy analyses, it should be modeled accurately. Weibull distribution has been used extensively to model variation of wind speed. Therefore, the most appropriate distribution parameter estimation Method selection is critical in order to minimize data set modeling errors. In this context, a novel, robust, efficient and better Method than standard Methods to estimate Weibull parameters is presented for the first time in this paper. The accuracy of the proposed Method is verified using different data sets. Also, developed Method is compared with Graphic Method (GM), Maximum Likelihood Method (MLM), Alternative Maximum Likelihood Method (AMLH), Modified Maximum Likelihood Method (MMLH), Moment Method (MM), Justus Moment Method (JMM), WAsP Method (WM) and Power Density Method (PD). The results indicate that the proposed novel Method is adequate to determine Weibull distribution parameters.
David W Hogg - One of the best experts on this subject based on the ideXlab platform.
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a Maximum Likelihood Method to improve faint source flux and color estimates
Publications of the Astronomical Society of the Pacific, 1998Co-Authors: David W Hogg, Edwin L TurnerAbstract:Flux estimates for faint sources or transients are systematically biased high because there are far more truly faint sources than bright. Corrections that account for this effect are presented as a function of signal- to-noise ratio and the (true) slope of the faint-source number-flux relation. The corrections depend on the source being originally identified in the image in which it is being photometered. If a source has been identified in other data, the corrections are different; a prescription for calculating the corrections is presented. Implications of these corrections for analyses of surveys are discussed; the most important is that sources identified at signal-to-noise ratios of 4 or less are practically useless.
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a Maximum Likelihood Method for improving faint source flux and color estimates
arXiv: Astrophysics, 1997Co-Authors: David W Hogg, Edwin L TurnerAbstract:Flux estimates for faint sources or transients are systematically biased high because there are far more truly faint sources than bright. Corrections which account for this effect are presented as a function of signal-to-noise ratio and the (true) slope of the faint-source number-flux relation. The corrections depend on the source being originally identified in the image in which it is being photometered. If a source has been identified in other data, the corrections are different; a prescription for calculating the corrections is presented. Implications of these corrections for analyses of surveys are discussed; the most important is that sources identified at signal-to-noise ratios of four or less are practically useless.
Lajos Horvath - One of the best experts on this subject based on the ideXlab platform.
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estimation in random coefficient autoregressive models
Journal of Time Series Analysis, 2006Co-Authors: Lajos Horvath, Josef SteinebachAbstract:We propose the quasi-Maximum Likelihood Method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions. Copyright 2006 Blackwell Publishing Ltd.
