With the help of continued fractions, we plan to list all the elements of the set Q△ = {aX2 ＋ bXY ＋ cY2 ： a,b, c ∈Z, b2 - 4ac = △ with 0 ≤ b 〈 √△}of quasi-reduced quadratic forms of fundamental discriminant ...With the help of continued fractions, we plan to list all the elements of the set Q△ = {aX2 ＋ bXY ＋ cY2 ： a,b, c ∈Z, b2 - 4ac = △ with 0 ≤ b 〈 √△}of quasi-reduced quadratic forms of fundamental discriminant △. As a matter of fact, we show that for each reduced quadratic form f = aX2 ＋ bXY ＋ cY2 = （a, b, c） of discriminant △〉0（and of sign σ（f） equal to the sign of a）, the quadratic forms associated with f and defined by {〈a＋bu＋cu2,b＋2cu.c〉},with 1≤σ（f）u≤b/2｜c｜ （whenever they exist）, 〈c,-b-2cu,a＋bu＋cu2〉 with b/2｜c｜≤σ（f）u≤[w（f）]=[b＋√△/2｜c｜], are all different from one another and build a set I（f） whose cardinality is #I（f）={1＋[ω（f）],when（2c）｜b,[ω（f）],when （2c）｜b. If f and g are two different reduced quadratic forms, we show that I（f） ∩ I（g） = Ф. Our main result is that the set Q△ is given by the disjoint union of all I（f） with f running through the set of reduced quadratic forms of discriminant △〉0. This allows us to deduce a formula for #（Q△） involving sums of partial quotients of certain continued fractions.展开更多
In this paper,we investigate the problem of estimating the probability density function.The kernel density estimation with bias reduced is nowadays a standard technique in explorative data analysis,there is still a bi...In this paper,we investigate the problem of estimating the probability density function.The kernel density estimation with bias reduced is nowadays a standard technique in explorative data analysis,there is still a big dispute on how to assess the quality of the estimate and which choice of bandwidth is optimal.This framework examines the most important bandwidth selection methods for kernel density estimation in the context of with bias reduction.Normal reference,least squares cross-validation,biased cross-validation andβ-divergence loss methods are described and expressions are presented.In order to assess the performance of our various bandwidth selectors,numerical simulations and environmental data are carried out.展开更多
文摘With the help of continued fractions, we plan to list all the elements of the set Q△ = {aX2 ＋ bXY ＋ cY2 ： a,b, c ∈Z, b2 - 4ac = △ with 0 ≤ b 〈 √△}of quasi-reduced quadratic forms of fundamental discriminant △. As a matter of fact, we show that for each reduced quadratic form f = aX2 ＋ bXY ＋ cY2 = （a, b, c） of discriminant △〉0（and of sign σ（f） equal to the sign of a）, the quadratic forms associated with f and defined by {〈a＋bu＋cu2,b＋2cu.c〉},with 1≤σ（f）u≤b/2｜c｜ （whenever they exist）, 〈c,-b-2cu,a＋bu＋cu2〉 with b/2｜c｜≤σ（f）u≤[w（f）]=[b＋√△/2｜c｜], are all different from one another and build a set I（f） whose cardinality is #I（f）={1＋[ω（f）],when（2c）｜b,[ω（f）],when （2c）｜b. If f and g are two different reduced quadratic forms, we show that I（f） ∩ I（g） = Ф. Our main result is that the set Q△ is given by the disjoint union of all I（f） with f running through the set of reduced quadratic forms of discriminant △〉0. This allows us to deduce a formula for #（Q△） involving sums of partial quotients of certain continued fractions.
文摘In this paper,we investigate the problem of estimating the probability density function.The kernel density estimation with bias reduced is nowadays a standard technique in explorative data analysis,there is still a big dispute on how to assess the quality of the estimate and which choice of bandwidth is optimal.This framework examines the most important bandwidth selection methods for kernel density estimation in the context of with bias reduction.Normal reference,least squares cross-validation,biased cross-validation andβ-divergence loss methods are described and expressions are presented.In order to assess the performance of our various bandwidth selectors,numerical simulations and environmental data are carried out.
