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Statements

Subject Item
n2:RIV%2F00216224%3A14310%2F03%3A00007953%21RIV09-GA0-14310___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
Nonparametric density estimates attempt to reconstruct the probability density from which a random sample has come, using the sample values and as few assumptions as possible about the density. These methods are smoothing operations on the sample distribution. Methods of kernel estimates represent one of the most effective nonparametric methods. These methods are simple to understand, easy to implement and they have very good mathematical properties. We employed the automatic procedure for the selection of the bandwidth, the kernel and the order of the kernel. This procedure is used for analysis of air temperature fluctuations for series of Central England and Prague-Klementinum in the periods 1661-2000 and 1771-2000, respectively. Graphical representation of the family of estimated densities in three dimensional space provide a better explanation of the long-term trends in temperature distribution of both series. Nonparametric density estimates attempt to reconstruct the probability density from which a random sample has come, using the sample values and as few assumptions as possible about the density. These methods are smoothing operations on the sample distribution. Methods of kernel estimates represent one of the most effective nonparametric methods. These methods are simple to understand, easy to implement and they have very good mathematical properties. We employed the automatic procedure for the selection of the bandwidth, the kernel and the order of the kernel. This procedure is used for analysis of air temperature fluctuations for series of Central England and Prague-Klementinum in the periods 1661-2000 and 1771-2000, respectively. Graphical representation of the family of estimated densities in three dimensional space provide a better explanation of the long-term trends in temperature distribution of both series. Nonparametric density estimates attempt to reconstruct the probability density from which a random sample has come, using the sample values and as few assumptions as possible about the density. These methods are smoothing operations on the sample distribution. Methods of kernel estimates represent one of the most effective nonparametric methods. These methods are simple to understand, easy to implement and they have very good mathematical properties. We employed the automatic procedure for the selection of the bandwidth, the kernel and the order of the kernel. This procedure is used for analysis of air temperature fluctuations for series of Central England and Prague-Klementinum in the periods 1661-2000 and 1771-2000, respectively. Graphical representation of the family of estimated densities in three dimensional space provide a better explanation of the long-term trends in temperature distribution of both series.
dcterms:title
Density estimate and its application to analysis of temperature series Density estimate and its application to analysis of temperature series Density estimate and its application to analysis of temperature series
skos:prefLabel
Density estimate and its application to analysis of temperature series Density estimate and its application to analysis of temperature series Density estimate and its application to analysis of temperature series
skos:notation
RIV/00216224:14310/03:00007953!RIV09-GA0-14310___
n4:aktivita
n5:Z n5:P
n4:aktivity
P(GA205/01/1067), Z(MSM 143100001)
n4:cisloPeriodika
1
n4:dodaniDat
n16:2009
n4:domaciTvurceVysledku
n9:3140075 n9:6007929 n9:5407656 n9:2811294
n4:druhVysledku
n10:J
n4:duvernostUdaju
n19:S
n4:entitaPredkladatele
n12:predkladatel
n4:idSjednocenehoVysledku
603067
n4:idVysledku
RIV/00216224:14310/03:00007953
n4:jazykVysledku
n14:eng
n4:klicovaSlova
kernel estimate; visualization; air temperature; Prague-Klementinum; Central England
n4:klicoveSlovo
n8:kernel%20estimate n8:visualization n8:Prague-Klementinum n8:Central%20England n8:air%20temperature
n4:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n4:kontrolniKodProRIV
[870B12A95CAD]
n4:nazevZdroje
Environmetrics
n4:obor
n17:BA
n4:pocetDomacichTvurcuVysledku
4
n4:pocetTvurcuVysledku
4
n4:projekt
n13:GA205%2F01%2F1067
n4:rokUplatneniVysledku
n16:2003
n4:svazekPeriodika
14
n4:tvurceVysledku
Budíková, Marie Zelinka, Jiří Brázdil, Rudolf Horová, Ivanka
n4:wos
000180856800007
n4:zamer
n15:MSM%20143100001
s:issn
1180-4009
s:numberOfPages
16
n18:organizacniJednotka
14310