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1  improved precision that tended to have less extreme values.
2 el solutions without the occurrence of large extreme values.
3 ariance, and their results are influenced by extreme values.
4 del parameters were adjusted to unreasonably extreme values.
5 nt and its enemies escalate to more and more extreme values.
6 ing settings with substantial nonoverlap and extreme values.
7 b pairs when the parents also have similarly extreme values.
8                            A global study of extreme value (1 in 100-year return period) tropical cyc
9 -CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GC
10                                We provide an extreme value analysis of daily new cases of COVID-19.
11                                  Here, using extreme value analysis, we find that the frequency of U.
12 nty; 2) it permits interpolation of both the extreme values and the extreme distribution parameters a
13 ries of the polymorphism data, and loci with extreme values are considered to be likely targets of po
14 datasets are diverse but of modest size, and extreme values are often of interest.
15 trality tests and present formulas for these extreme values as a function of sample size and number o
16               We classified individuals with extreme values as having epigenetic age acceleration (EA
17 luorescence resonance energy transfer) or an extreme value (as in cyclization), and in principle prov
18                                      Results Extreme values aside, results of histogram analysis of A
19 lidate an efficient approach, referred to as extreme value-based emitter recovery (EVER), to accurate
20     Compiled nonmonophyly rates are probably extreme values, because molecular analyses have focused
21  been justified by the statistical theory of extreme values, because the fitnesses conferred by benef
22 or binding-site matching by SOIPPA follow an extreme value distribution (EVD).
23        These estimates are derived using the extreme value distribution from the mean and variance of
24                                           An extreme value distribution model estimates the statistic
25 approximations for both the distribution and extreme value distribution of similarity scores.
26 ov-Smirnov tests showed that the generalized extreme value distribution provided an adequate fit for
27                              The generalized extreme value distribution was fitted to maximum rainfal
28                              The generalized extreme value distribution was fitted to them with two o
29  alignments do not follow the classic Gumbel extreme value distribution, we propose a novel distribut
30 anscript isoforms to follow the same Weibull extreme value distribution.
31 are described excellently by the generalized extreme value distribution.
32  that characterizes the binding signal as an extreme value distribution.
33 on new unbiased estimators for parameters of extreme value distribution.
34 e PDB and found that the TM-scores follow an extreme value distribution.
35 n of attraction of the so-called Gumbel-type extreme value distribution.
36 approximate the parameters of the underlying extreme value distribution.
37  using the local alignment version follow an extreme value distribution.
38 y our generalised scoring matrix followed an extreme value distribution; this yielded accurate estima
39       Furthermore, we show that by using the extreme-value distribution to characterize genomic regio
40 ity of essentiality for each gene, using the extreme-value distribution to characterize the statistic
41 is formula means it is unnecessary to fit an extreme-value distribution to simulations or to the resu
42   Structure comparison scores also follow an extreme-value distribution when the statistics are expre
43     These scores can be well described by an extreme-value distribution.
44 t the scores for sequence matching follow an extreme-value distribution.
45 tionary frequency analyses using generalized extreme value distributions on 30-year rolling periods f
46                    We model this behavior by extreme value distributions with parameters that are lin
47 ity of occurrence, also in the context of ST extreme value distributions, and we conclude that rogue
48    We find that the similarity scores follow extreme value distributions.
49                                       We fit extreme-value distributions for fixed lengths of combine
50  While acknowledging limitations in handling extreme values, especially in regions with low DIR, our
51 ses, four model variables must be changed to extreme values for the cost-utility of annual screening
52               The results show a generalized extreme value (GEV) distribution consistently outperform
53                       We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal str
54  level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the r
55 nt stationary and non-stationary generalized extreme value (GEV) models, and a random sampling techni
56 a problem, because the scores do not fit the extreme-value (Gumbel) distribution commonly used to est
57  with occult or obvious malignancy may be of extreme value in the detection and management of cancer
58 B-RW resampling strategy designed to improve extreme values in an imbalanced regression dataset, with
59                             The influence of extreme values in the background data set can also be tu
60 quivalent and may be unavoidable at the most extreme values in this population.
61            Contributing factors that lead to extreme values include high geopolitical concentration o
62 s of immuno-metabolic markers, and increased extreme-valued inflammatory markers.
63 viduals producing judgments that tend toward extreme values instead.
64 cean current velocities and especially their extreme values is necessary to understand geomorphology
65 h and to elicit specific stress responses at extreme values; it is often used as a genetic tool to in
66 s, further demonstrating the accuracy of the extreme value model.
67 ation (also called the 'Weibull' or 'Weibull extreme value' model) infers time to extinction from a t
68                                     When the extreme value of the density profile reaches rho = 0.5,
69 isplays significant regional variations with extreme values of 22% in the central gastrocnemius.
70 pessimistic and optimistic scenarios provide extreme values of 5.4-122 g CO(2) equiv/kWh.
71 gy is to sequence only the subjects with the extreme values of a quantitative trait.
72 l HWEs is related to the distribution of the extreme values of a wave-runup parameter, [Formula: see
73 uate different approaches to describing more extreme values of body mass index (BMI)-for-age by using
74 nically characterized research subjects with extreme values of CSF Abeta levels.
75 ce for a counter-Bayesian strategy was under extreme values of individual stimuli within sequences, a
76       We find that side-chain carboxyls with extreme values of koff or kon are involved in hydrogen b
77 low heterogeneity is closely correlated with extreme values of local strain bursts that are not readi
78                                              Extreme values of Psi0 lead to asymmetric, bell-shaped e
79 didate genes or pathways in individuals with extreme values of quantitative phenotypes.
80 ective strategy is to sequence subjects with extreme values of quantitative traits or those with spec
81 e shifts in the probability distributions of extreme values of the Arctic and North Atlantic Oscillat
82 present a survey of methods for establishing extreme values of the group velocity, concentrating espe
83 t cancer was greater than 2 scans/year; less extreme values of these parameters imply little risk att
84                           Examination of the extreme values of these ratios indicates that probes wit
85                               The median and extreme values of these variables were computed providin
86 ntile for gestational age and postnatal day (extreme value) on at least 1 of the first 3 postnatal da
87    A second experiment in which one or other extreme-valued option was omitted from the learning sequ
88                                     Removing extreme values or adjustment for gender, cigarette smoki
89 , these data tables are often corrupted with extreme values (outliers), missing values, and non-norma
90 election on hemoglobin concentration because extreme values predicted fewer livebirths and directiona
91 er potency that is technically similar to an extreme value statistic.
92                                          The extreme value statistics based method, while more genera
93 d an efficient algorithm for calculating the extreme value statistics for peptide identification appl
94 ial photosynthesis using currently-available extreme value statistics photon sources.
95  of SARS-CoV and SARS-CoV-2 transmission and extreme value statistics to show that the distribution o
96                                        Using extreme value statistics, here we show where regional te
97 s that can be quantified in the framework of extreme value statistics.
98 This also leads to Frechet instead of Gumbel extreme value statistics.
99                      In addition, the Gumbel extreme-value statistics are applied.
100 s at tide-gauge sites across the globe using extreme-value statistics.
101 e discount rate are simultaneously varied to extreme values that bias the analysis against surgery.
102 stribution tail models are constructed using extreme value theory (EVT) and data on 33-y events.
103                                          The extreme value theory (EVT) has been widely used in study
104                                              Extreme value theory (EVT) is a statistical method to ch
105 istically robust estimation methods based on extreme value theory (EVT); and assess the implications
106                          Here, we conduct an extreme value theory analysis of weather station observa
107 n by using two indicators based on combining extreme value theory and dynamical systems: the instanta
108             This has been justified by using extreme value theory and, in particular, by assuming tha
109  species, our interpretations and use of the extreme value theory are general and can be widely appli
110 vide some support for the use of Gumbel-type extreme value theory in studies of adaptation and point
111 w an elementary probabilistic model based on extreme value theory rationalizes the latter finding.
112                          While the appeal to extreme value theory seems justified, the exponential di
113                                     However, extreme value theory shows that two other domains of att
114                                  Here we use extreme value theory to combine sea-level projections wi
115 eory for the species-area relationship using extreme value theory, and show that the species-area rel
116                                  By applying extreme value theory, Gillespie circumvented this issue
117                                        Using extreme value theory, I derive this distribution and sho
118 part because it can be justified in terms of extreme value theory, since beneficial mutations should
119                                       Yet in extreme value theory, there are three different limiting
120 ves residual energy scaling, consistent with extreme value theory.
121                        In this study, we use extreme-value theory to explore the distribution of natu
122           This clears the way for asymptotic extreme-value theory to guarantee: (1) a non-increasing
123                              We use analytic extreme value thresholds to identify a new class of indi
124 ugh we do not advocate hemodilution to these extreme values, we find that these data provide a physio
125 ghest incidence of co-occurrence and contain extreme values well above their local 95th percentile th
126 ucan levels above 6.7% and 10 below 3.6%.The extreme values were 1.8% and 7.5%.
127                                              Extreme values were defined as monthly maximums of daily
128 erals, and statistically significant (P<.05) extreme values were reported for 14 of the 31 minerals t
129 d wide variability (range -0.04 to 1.0), and extreme values were seen in 34.5% of the group (<0.10 in
130 lds in the NHANES, which is a study in which extreme values were verified when recorded.
131 irst slowly increases and then points toward extreme values when the reproductive system tends toward
132                 This principle suggests that extreme values will moderate the next time they are reco

 
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