random.longonly(rportfolios)
random.longonly()所属R语言包：rportfolios
Random long only portfolio
随机长仅投资组合
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function generates a vector of investment weights for a portfolio where the weights are non-negative, do not exceed a given upper and and the sum of the weights is a given total. The number of non zero positions is k.
这个函数生成一个投资权重向量的组合，其中权重都是非负的，不超过一个给定的上和的权重的总和是一个给定的总。非零位置的数目为k。
用法----------Usage----------
random.longonly(n = 2, k=n, x.t = 1, x.l=0,
x.u = x.t, max.iter = 1000)
参数----------Arguments----------
参数：n
An integer value for the number of investments in the portfolio
一个整数值组合中的投资
参数：k
An integer value for the number of non zero weights
一个整数值，非零权数
参数：x.t
Numeric value for the sum of the investment weights
的数字值的总和的投资权重
参数：x.l
Numeric value for the lower bound on an investment weight
数字上的投资权重值的下限
参数：x.u
Numeric value for the upper bound on an investment weight
数值上的投资权重上限
参数：max.iter
An integer value for the maximum iteration in the acceptance rejection loop
一个整数值，在接受拒绝的最大迭代循环
Details
详细信息----------Details----------
The simulation methods combines the acceptance rejection method used for generating gamma and gaussian random variables with a continuous analog of the method used in Ross (2006) to generate a vector of multinomial random variables. n - 1 random variables are constructed where the first U_1 is uniformly distributed in the interval ≤ft[ X_l, X_t \right]. Random variable U_2 is a uniform random variable in ≤ft[ {X_l,X_t - U_1 } \right] given U_1. Random variable U_3 is a uniform random variable in ≤ft[ {0,X_t - U_1 - U_2 } \right] given U_1 and U_2. This conditional generation of uniform random variables stops with U_{n - 1} which is uniform on ≤ft[ {X_l,X_t - ∑\limits_{j = 1}^{n - 2} {U_j } } \right] given the first n - 2 random variables. if X_t - ∑\limits_{j = 1}^{n - 1} {U_j } is less than or equal to X_u, then the final random variable is U_n = X_t - ∑\limits_{j = 1}^{n - 1} {U_j }. Otherwise, the above procedure of generating uniform random variables conditionally is repeated until this condition is satisfied. The vector {\mathbf{W}} is a random sample of size n of the values in vector {\mathbf{U}} where the sampling is performed without replacement.
仿真方法结合了接受拒绝使用的方法，用于产生γ和罗斯（2006年）中所使用的方法来生成多项式的随机变量的矢量与一个连续的模拟的高斯随机变量。 n - 1构造随机变量的第一U_1被均匀地分布在区间≤ft[ X_l, X_t \right]。随机变量U_2是均匀分布的随机变量的≤ft[ {X_l,X_t - U_1 } \right]给U_1的。随机变量U_3是均匀分布的随机变量≤ft[ {0,X_t - U_1 - U_2 } \right]给定的U_1和U_2。有条件地生成均匀分布的随机变量停止U_{n - 1}是一致的≤ft[ {X_l,X_t - ∑\limits_{j = 1}^{n - 2} {U_j } } \right]第一n - 2随机变量。 X_t - ∑\limits_{j = 1}^{n - 1} {U_j }如果是小于或等于X_u，那么最终的随机变量是U_n = X_t - ∑\limits_{j = 1}^{n - 1} {U_j }。否则，产生均匀的随机变量有条件上述程序被重复进行，直到满足该条件。向量{\mathbf{W}}是一个随机样本的大小n中值的向量{\mathbf{U}}进行采样无需更换。
值----------Value----------
A numeric vector with investment weights.
一个数字矢量投资权重。
（作者）----------Author(s)----------
Frederick Novomestky <a href="mailto:fnovomes@poly.edu">fnovomes@poly.edu</a>
参考文献----------References----------
Journal of the Royal Statistical Society, Series C (Applied Statistics), 26(1), 71.
Journal of the American Statistical Association, December 1976, 71(356), 893.
6(3), July 1964, 260-264.
of the ACM, 21 (11), November 1978, 925-928.
实例----------Examples----------
###[＃＃]
### long only portfolio of 30 investments with 30 non-zero positions[＃＃30投资组合与30个非零位]
###[＃＃]
x <- random.longonly( 30 )
###[＃＃]
### long only portfolio of 30 investments with 10 non-zero positions[＃＃30投资组合与10个非零位]
###[＃＃]
y <- random.longonly( 30, 10 )
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-9-28 20:06:36
