Bayesian sieve method for piece-wise smooth regression

2017 ◽  
Vol 130 ◽  
pp. 5-11
Author(s):  
Taihe Yi ◽  
Zhengming Wang
Keyword(s):  
Sieve Method ◽  
Smooth Regression

scholarly journals Technical Note: On methodologies for determining the size-normalised weight of planktic foraminifera

2010 ◽  
Vol 7 (7) ◽  
pp. 2193-2198 ◽  
Author(s):  
C. J. Beer ◽  
R. Schiebel ◽  
P. A. Wilson
Keyword(s):  
Wall Thickness ◽  
North Atlantic ◽  
Technical Note ◽  
Sieve Method ◽  
Planktic Foraminifera ◽  
Test Size ◽  
The North ◽  
Sieve Fraction ◽  
Error Bar ◽  
Fraction Method

Abstract. The size-normalised weight (SNW) of planktic foraminifera, a measure of test wall thickness and density, is potentially a valuable palaeo-proxy for marine carbon chemistry. As increasing attention is given to developing this proxy it is important that methods are comparable between studies. Here, we compare SNW data generated using two different methods to account for variability in test size, namely (i) the narrow (50 μm range) sieve fraction method and (ii) the individually measured test size method. Using specimens from the 200–250 μm sieve fraction range collected in multinet samples from the North Atlantic, we find that sieving does not constrain size sufficiently well to isolate changes in weight driven by variations in test wall thickness and density from those driven by size. We estimate that the SNW data produced as part of this study are associated with an uncertainty, or error bar, of about ±11%. Errors associated with the narrow sieve fraction method may be reduced by decreasing the size of the sieve window, by using larger tests and by increasing the number tests employed. In situations where numerous large tests are unavailable, however, substantial errors associated with this sieve method remain unavoidable. In such circumstances the individually measured test size method provides a better means for estimating SNW because, as our results show, this method isolates changes in weight driven by variations in test wall thickness and density from those driven by size.

Selberg's sieve method (continued)

2010 ◽  
pp. 43-66
Author(s):  
Harold G. Diamond ◽  
H. Halberstam ◽  
William F. Galway
Keyword(s):  
Sieve Method ◽  
Selberg’S Sieve Method ◽  
Selberg's Sieve

scholarly journals DIOPHANTINE APPROXIMATION BY PRIMES

2009 ◽  
Vol 52 (1) ◽  
pp. 87-106 ◽  
Author(s):  
KAISA MATOMÄKI
Keyword(s):  
Diophantine Approximation ◽  
Necessary Conditions ◽  
Circle Method ◽  
Sieve Method

AbstractWe show that whenever δ > 0 and constants λi satisfy some necessary conditions, there are infinitely many prime triples p1, p2, p3 satisfying the inequality |λ0 + λ1p1 + λ2p2 + λ3p3| < (max pj)−2/9+δ. The proof uses Davenport–Heilbronn adaption of the circle method together with a vector sieve method.

Six Primes and an Almost Prime in Four Linear Equations

1998 ◽  
Vol 50 (3) ◽  
pp. 465-486 ◽  
Author(s):  
Antal Balog
Keyword(s):  
Asymptotic Formula ◽  
Linear Equations ◽  
Arithmetic Mean ◽  
Sieve Method ◽  
Arithmetic Means ◽  
Almost Prime ◽  
Hardy Littlewood Method

AbstractThere are infinitely many triplets of primes p, q, r such that the arithmetic means of any two of them, are also primes. We give an asymptotic formula for the number of such triplets up to a limit. The more involved problem of asking that in addition to the above the arithmetic mean of all three of them, is also prime seems to be out of reach. We show by combining the Hardy-Littlewood method with the sieve method that there are quite a few triplets for which six of the seven entries are primes and the last is almost prime.

Improved Sieve Method for Measurement of Lyophoresis in Applesauce

1984 ◽  
Vol 49 (6) ◽  
pp. 1614-1615
Author(s):  
J. N. NOGUEIRA ◽  
M. R. McLELLAN
Keyword(s):  
Sieve Method

scholarly journals Function Field Sieve Method for Discrete Logarithms over Finite Fields

1999 ◽  
Vol 151 (1-2) ◽  
pp. 5-16 ◽  
Author(s):  
Leonard M. Adleman ◽  
Ming-Deh A. Huang
Keyword(s):  
Finite Fields ◽  
Function Field ◽  
Sieve Method ◽  
Discrete Logarithms
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