复合函数定义域的求法

1.复合函数的定义设u=g(x)是A到B的函数,y=f(u)是B′到C′上的函数,且BB′,当u取遍B中的元素时,y取遍C(CC′),那么y=f(g(x))就是A到C上的函数.此函数称为由外层函数y=f(x)和内层函数u=g(x)复合而成的复合函数,其中x称为直接变量,u称为中间变量,u的取值范围即为g(x)的值域.     

名校基础知识自测 高一年级

一、选择题1.集合M={(x,y)|y=f(x),x∈A}∩{(x,y)|x= 1}(A R)的元素个数为( ). (A)0 (B)1 (C)2 (D)0或12.下列函数中与y=x表示同一函数的是( ). (A)y=x2/x (B)y=(√x)2 (C)y=√x2 (D)y=x53.函数f(x)的定义域为[-1,2],则函数F(x)= f(x) f(-x)的定义域是( ). (A)[-1,2] (B)[-2,1] (C)[-1,1] (D)[-2,2]     

考点5 反函数

1.(江苏卷,2)函数y=21-x+3(x∈R)的反函数的解析表达式为().(A)y=log2x-23(B)y=log2x-23(C)y=log23-2x(D)y=log23-2x2.(山东卷,2)函数y=1-x x(x≠0)的反函数的图像大致是().(A)(B)(C)(D)3.(全国卷,3)函数y=3x2-1(x≤0)的反函数是().(A)y=(x+1)3(x≥-1)(B)y=-(x+1)3(x≥-1)(C)y=(x+1)3(x≥0)(D)y=-(x+1)3(x≥0)4.(辽宁卷,5)函数y=ln(x+x2+1)的反函数是().(A)y=ex+2e-x(B)y=-ex+2e-x(C)y=ex-2e-x(D)y=-ex-2e-x5.(天津卷,9)设f-1(x)是函数f(x)=12(ax-a-x)(a>1)的反函数,则使f-1(x)>1成立的x的取值范围为().(A)(a22-a1,+∞)(B)(-∞,a22-…     

新题征展(15)

A.题组新编1.(1)函数f(x)=x|x|的反函数为　　;(2)函数f(x)=x|x| x-1的反函数为　　;(3)函数f(x)=x|x|-x-1　　反函数(填“有”或“无”);(4)由方程x|x| y|y|=1确定函数y=f(x),则f(x)在(-∞, ∞)上是(　　).　(A)增函数　　　　(B)减函数　(C)奇函数(D)偶函数2.(1)两圆C1:x2 y2 4x-4y 7=0,C2:x2 y2-4x-10y 13=0的公切线有(　　).　(A)1条　(B)2条　(C)3条　(D)4条(2)过定点P(1,2)且与两坐标轴围成的三角形面积等于4的直线有(　　).　(A)1条　(B)2条　(C)3条　(D)4条(3)与圆x2-4x y2 2=0相切且在两坐标轴截距相等的直线有(　　).　(A)…     

用点到直线距离公式解决与函数相关的问题

点P(x,y)到直线Ax By C=0距离为d=|Ax By C|/A~2 B~2,当P(x,y)在函数y=f(x)上时,该公式变为d=|Ax Bf(x) C|/A~2 B~2,本文通过引进函数y=f(x),借助该公式解决一些与函数相关的问题.1.求函数单调性例1求f(x)=|x 2-1-x2|的单调区间及单调性.分析把函数f(x)作为点线间距离,借助图象,看x变大时,该距离如何变?图1例1图解函数的定义域是-1≤x≤1,令y=1-x2,即x2 y2=1,y≥0.如图1,所以f(x)=|x 2-y|=|x 2-y|2×2,几何意义:半圆上动点M(x,y)到定直线l:x-y 2=0的距离的2倍.由图1知使OB⊥l时,B到l的距离最小,显然OB:y=-x,由x2 y2=1,(y≥0),y=-x,…     

函数图象对称性选择题

1.函数y=f(x)与y=-f~-1(-x)的图象( )。 (A)关于y=x对称 (B)关于y=-x对称 (c)关于x轴对称 (D)关于原点对称 2.设函数y=f(x)与y=-f(x)的图象既关于x轴对称,又关于原点对称,那么y=f(x)图象( )。 (A)关于x轴成轴对称图形 (B)关于y轴成轴对称图形 (C)关于原点成中心对称图形 (D)关于直线y=x成轴对称图形     

全微分方程的不定积分解法及其证明

0　引言一个一阶微分方程写成P( x,y) dx +Q( x,y) dy =0 ( 1 )形式后 ,如果它的左端恰好是某一个函数 u=u( x,y)的全微分 :du( x,y) =P( x,y) dx +Q( x,y) dy那么方程 ( 1 )就叫做全微分方程。这里 u x=P( x,y) ,　　 u y=Q( x,y)方程 ( 1 )就是 du( x,y) =0 ,其通解为 :u( x,y) =C　 ( C为常数 )可见 ,解全微分方程的关键在于求原函数 u( x,y)。因此 ,本文将提供一种求原函数 u( x,y)的简捷方法 ,并给出证明。1　引入记号为了表述方便 ,先引入记号如下 :设 M( x,y)为一个含有变量 x,y项的二元函数 ,定义 :( 1 )“M( x,y)”表示 M(…     

用点到直线距离公式解决与函数相关的问题

点P（x，y）到直线Ax＋By＋C=0距离为d=｜Ax＋By＋C｜/√A^2＋B^2,当P（x，y）在函数y=f（x）上时，该公式变为d=｜Ax＋Bf（x）＋C｜/√A^2＋B^2,本文通过引进函数y=f（x），借助该公式解决一些与函数相关的问题．     

关于求解第二类Volterra积分方程的Picard迭代收敛性的一点注记

考虑第二类Volterra积分方程: φ(x)+integral from n=0 to x(K(x,y)φ(y)dy)=f(x),x∈[0,L],(1)其中f(x)∈C([0,L]),核函数 K(x,y)对y可积,且     

求函数值域慎用“平方”法

当遇到含二次根式且定义域为R的函数求值域时,有时虽可通过平方法将函数关系式转化为关于x的类二次方程"A(y)x2+B(y)x+C(y)=0(x∈R)"的形式,再结合根的判别式来求得答案,但这种方法通常会扩大函数值的取值范围,导致结果出错.     

Construction of asymmetric multivariate copulas

In this paper we introduce two methods for the construction of asymmetric multivariate copulas. The first is connected with products of copulas. The second approach generalises the Archimedean copulas. The resulting copulas are asymmetric and may have more than two parameters in contrast to most of the parametric families of copulas described in the literature. We study the properties of the proposed families of copulas such as the dependence of two components (Kendall’s tau, tail dependence), marginal distributions and the generation of random variates.     

A class of multivariate copulas with bivariate Fréchet marginal copulas

In this paper, we present a class of multivariate copulas whose two-dimensional marginals belong to the family of bivariate Fréchet copulas. The coordinates of a random vector distributed as one of these copulas are conditionally independent. We prove that these multivariate copulas are uniquely determined by their two-dimensional marginal copulas. Some other properties for these multivariate copulas are discussed as well. Two applications of these copulas in actuarial science are given.     

From Archimedean to Liouville copulas

We use a recent characterization of the d-dimensional Archimedean copulas as the survival copulas of d-dimensional simplex distributions (McNeil and Nešlehová (2009) [1]) to construct new Archimedean copula families, and to examine the relationship between their dependence properties and the radial parts of the corresponding simplex distributions. In particular, a new formula for Kendall’s tau is derived and a new dependence ordering for non-negative random variables is introduced which generalises the Laplace transform order. We then generalise the Archimedean copulas to obtain Liouville copulas, which are the survival copulas of Liouville distributions and which are non-exchangeable in general. We derive a formula for Kendall’s tau of Liouville copulas in terms of the radial parts of the corresponding Liouville distributions.     

Copulas and associated fractal sets

The problem of constructing copulas whose supports are fractals has been studied by Fredricks, Nelsen and Rodríguez-Lallena [G.A. Fredricks, R.B. Nelsen, J.A. Rodríguez-Lallena, Copulas with fractal supports, Insurance Math. Econom. 37 (1) (2005) 42–48]. In this paper we continue on the path traced by these authors. We provide different types of families of self-similar copulas using techniques from Probability and Ergodic Theory to give properties on subsets of their fractal supports. In particular, we give new examples for those copulas and we analyze related topics with mutual singularity of the associated measures, Hausdorff dimension, and the connectedness of their supports.     

Tail order and intermediate tail dependence of multivariate copulas

In order to study copula families that have tail patterns and tail asymmetry different from multivariate Gaussian and t copulas, we introduce the concepts of tail order and tail order functions. These provide an integrated way to study both tail dependence and intermediate tail dependence. Some fundamental properties of tail order and tail order functions are obtained. For the multivariate Archimedean copula, we relate the tail heaviness of a positive random variable to the tail behavior of the Archimedean copula constructed from the Laplace transform of the random variable, and extend the results of Charpentier and Segers [7] [A. Charpentier, J. Segers, Tails of multivariate Archimedean copulas, Journal of Multivariate Analysis 100 (7) (2009) 1521–1537] for upper tails of Archimedean copulas. In addition, a new one-parameter Archimedean copula family based on the Laplace transform of the inverse Gamma distribution is proposed; it possesses patterns of upper and lower tails not seen in commonly used copula families. Finally, tail orders are studied for copulas constructed from mixtures of max-infinitely divisible copulas.     

Constructing hierarchical Archimedean copulas with Lévy subordinators

A probabilistic interpretation for hierarchical Archimedean copulas based on Lévy subordinators is given. Independent exponential random variables are divided by group-specific Lévy subordinators which are evaluated at a common random time. The resulting random vector has a hierarchical Archimedean survival copula. This approach suggests an efficient sampling algorithm and allows one to easily construct several new parametric families of hierarchical Archimedean copulas.     

Multivariate Hierarchical Copulas with Shocks

A transformation to obtain new multivariate hierarchical copulas, starting with an arbitrary copula, is introduced. In addition to the hierarchical structure, the presented construction principle explicitly supports singular components. These may be interpreted as the effect of local or global shocks to the underlying random variables. A large spectrum of dependence patterns can be achieved by the presented transformation, which seems promising for practical applications. Moreover, copulas arising from this construction are similarly admissible with respect to analytical tractability and sampling routines as the original copula. Finally, several well-known families of copulas may be interpreted as special cases.     

Tail dependence functions and vine copulas

Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its extreme value copulas, is shown to be completely determined by its tail dependence functions. For a vine copula built from a set of bivariate copulas, its tail dependence function can be expressed recursively by the tail dependence and conditional tail dependence functions of lower-dimensional margins. The effect of tail dependence of bivariate linking copulas on that of a vine copula is also investigated.     

A scalar product for copulas

We introduce a scalar product for n-dimensional copulas, based on the Sobolev scalar product for W1,2-functions. The corresponding norm has quite remarkable properties and provides a new, geometric framework for copulas. We show that, in the bivariate case, it measures invertibility properties of copulas with respect to the ∗-operation introduced by Darsow et al. (1992). The unique copula of minimal norm is the null element for the ∗-operation, whereas the copulas of maximal norm are precisely the invertible elements.     

Spatially homogeneous copulas

We consider spatially homogeneous copulas, i.e. copulas whose corresponding measure is invariant under a special transformations of \([0,1]^2\), and we study their main properties with a view to possible use in stochastic models. Specifically, we express any spatially homogeneous copula in terms of a probability measure on [0, 1) via the Markov kernel representation. Moreover, we prove some symmetry properties and demonstrate how spatially homogeneous copulas can be used in order to construct copulas with surprisingly singular properties. Finally, a generalization of spatially homogeneous copulas to the so-called (m, n)-spatially homogeneous copulas is studied and a characterization of this new family of copulas in terms of the Markov \(*\)-product is established.