Extreme Value Distributions

Kotz, Samuel; Nadarajah, Saralees

doi:10.1142/p191

Cited by 748 publications

(213 citation statements)

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“…Many sources provide background on the extreme value distributions [14][15][16][17] . In my own work, I described the technical details for a maximum entropy interpretation of extreme values 3,18 , and the scale transformations that connect extreme value forms to general measurement interpretations of probability patterns 4 .…”

Section: B Extreme Values: Dissipation On the Cumulative Scalementioning

confidence: 99%

How to Read Probability Distributions as Statements About Process

Frank

2014

SSRN Journal

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Probability distributions can be read as simple expressions of information. Each continuous probability distribution describes how information changes with magnitude. Once one learns to read a probability distribution as a measurement scale of information, opportunities arise to understand the processes that generate the commonly observed patterns. Probability expressions may be parsed into four components: the dissipation of all information, except the preservation of average values, taken over the measurement scale that relates changes in observed values to changes in information, and the transformation from the underlying scale on which information dissipates to alternative scales on which probability pattern may be expressed. Information invariances set the commonly observed measurement scales and the relations between them. In particular, a measurement scale for information is defined by its invariance to specific transformations of underlying values into measurable outputs. Essentially all common distributions can be understood within this simple framework of information invariance and measurement scale ab .

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Section: B Extreme Values: Dissipation On the Cumulative Scalementioning

confidence: 99%

How to Read Probability Distributions as Statements About Process

Frank

2014

SSRN Journal

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“…풍력단지 건설시 터빈 등급은 평균풍속이 아니라 극한풍속 을 기준으로 선정되기 때문에 Table 3에는 측정 데이터 및 Gumbel distribution을 이용하여 산출된 극한풍속을 도시하 였다 (9) . 4m 높이에서 실측된 데이터를 기준으로 1년 및 50년…”

Section: 극치풍속unclassified

Assessment of Wind Resource Around the Korean Peninsula by Using Marine Buoys Datasets

Kim²,

Lee³

2011

Journal of The korean society for new and renewable energy

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In recent years, many countries have been endeavoring to exploit the offshore wind energy in terms of overcoming the limitations of on-land wind energy. Considering that mountains cover 70 percent of the Korean Peninsula and arable plains for wind energy are negligibly small, Korean government aggressively drives the offshore wind development of the Korean Peninsula. As part of preliminary investigation of offshore wind resources, KEPCO-RI (Korea Electric Power Corporation-Research Institute) has been analyzing marine buoy datasets measured at 5 positions over the period of 12 years, including estimation of extreme wind speed. It can be observed that variation of yearly wind speed, monthly wind speed as well as frequency distribution of wind direction. Wind classes of buoy sites are estimated by extrapolated average wind speed using log law. In addition, wind turbine class based on IEC code is assessed for evaluation of suitable wind turbine. Key words Offshore Wind Farm(해상풍력단지), Marine Buoy(해상 부이), Wind Resource Assessment(바람자원 평가), Extreme WindSpeed(극치풍속), Offshore Wind Turbine(해상풍력터빈) (접수일 2011. 2. 23, 1차 수정일 2011. 3. 4, 게재확정일 2011. 3. 4) * , ** 한전 전력연구원 ■ E-mail : okyer@kepri.re.kr ■ Tel : (042)

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“…The following result is classical in the asymptotic theory of order statistics (for proofs, discussions and generalizations, see [2,3,[5][6][7]). …”

Section: Some Facts From the Asymptotic Theory Of Order Statisticsmentioning

confidence: 99%