<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[dreamcatcher33的专栏]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/dreamcatcher33</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; dreamcatcher33]]></copyright><item><title><![CDATA[Linear Regression(Chapter 6)[@XiruChen]]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44815693</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44815693</guid><author>dreamcatcher33</author><pubDate>Wed, 01 Apr 2015 22:35:07 +0800</pubDate><description><![CDATA[Outlines
Data, Model and Assumptions
Parameter estimation
Important statistics and distribution
Multivariate linear regression
Data, Model and AssumptionsDatann observations: (X1,Y1),(X2,Y2),...,(Xn,Yn]]></description><category></category></item><item><title><![CDATA[Plan of 2015]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44783145</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44783145</guid><author>dreamcatcher33</author><pubDate>Tue, 31 Mar 2015 18:20:16 +0800</pubDate><description><![CDATA[Keywords
plan & summary writing;
Reading book/paper;
Project;
Drug Development;
English;
RequirementsWrite week plan and week summary! 
Obey the tomato working protocol~Book list of 2015
Probability an]]></description><category></category></item><item><title><![CDATA[Multivariate Analysis ch1 (Overview)]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44230171</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44230171</guid><author>dreamcatcher33</author><pubDate>Thu, 12 Mar 2015 23:38:58 +0800</pubDate><description><![CDATA[This is the note of book “Applied multivariate methods for data analysts”1.Outlines:
Data: Response Variable v.s. Experimental Units;
Type of Methods: Variable-directed techinique v.s. Individual-direc]]></description><category></category></item><item><title><![CDATA[CCA(gwas)]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44199619</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44199619</guid><author>dreamcatcher33</author><pubDate>Wed, 11 Mar 2015 15:36:37 +0800</pubDate><description><![CDATA[This is a short review of a multivariate test of associations[@ferreira2009multivariate] implemented in PLINK[@Purcell2007559].Remained Questions:
Derivation of the statistics still don’t understand;
C]]></description><category></category></item><item><title><![CDATA[Permutation test]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197501</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197501</guid><author>dreamcatcher33</author><pubDate>Wed, 11 Mar 2015 13:23:37 +0800</pubDate><description><![CDATA[Outlines:
Goals and strategy
The simple condition
The complex condition
Goals and strategyGoals:
Estimate the distribution of the test statistic(TT) under the null hypothesis, thus the p-value of the o]]></description><category></category></item><item><title><![CDATA[Fisher Information]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197491</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197491</guid><author>dreamcatcher33</author><pubDate>Wed, 11 Mar 2015 13:20:49 +0800</pubDate><description><![CDATA[1. The Likelihood
似然函数: 设XX的p.d.f.为f(x;θ)f(x;\theta)，其中θ\theta为参数，i.i.d.样本X˜=(X1,...,Xn)\widetilde{X}=(X_1,...,X_n),那么似然函数为： 
L(θ;x˜)=∏i=1nf(xi;θ).L(\theta;\widetilde{x})= \prod_{i=1}^n f(x_i;\theta).]]></description><category></category></item><item><title><![CDATA[Interval estimation (Section 4.4)[@XiruChen]]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197469</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197469</guid><author>dreamcatcher33</author><pubDate>Wed, 11 Mar 2015 13:18:36 +0800</pubDate><description><![CDATA[0. Outlines
区间构造原则
置信系数；
区间估计方法简介：(Confidence Interval v.s. Critical Interval)[@wiki_CI] 
小样本估计法（枢轴量法）
大样本估计法(近似正态) 
非参数bootstrap法
贝叶斯方法
区间估计的评估
1. 区间估计构造原则对于构造的估计区间[θ^1(X1,...,Xn),θ^2(X1,...,Xn)][\hat]]></description><category></category></item><item><title><![CDATA[Hypothesis Testing(Chapter 5)[@XiruChen]]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197455</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197455</guid><author>dreamcatcher33</author><pubDate>Wed, 11 Mar 2015 13:18:29 +0800</pubDate><description><![CDATA[0. Outlines1. 基本概念
假设，检验统计量，拒绝域；
功效函数；
两类错误，检验的水平；
假设检验的优良性准则（一致最优检验）；
p-值；
2. 重要的参数检验连续型随机变量：
正态总体均值的检验；
两正态总体均值差的检验；
正态总体方差的检验（一个总体&两个总体）；
指数分布参数的检验；
离散型随机变量
二项分布参数pp的检验(符号检验)；
泊松分布参数λ\lambda的检验；
大样本]]></description><category></category></item><item><title><![CDATA[Parameter Estimation (Chapter 4)[@XiruChen]]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197435</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44197435</guid><author>dreamcatcher33</author><pubDate>Wed, 11 Mar 2015 13:15:10 +0800</pubDate><description><![CDATA[1. 数理统计学框架]]></description><category></category></item><item><title><![CDATA[Probability and statistics (part I)]]></title><link>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44196829</link><guid>https://blog.youkuaiyun.com/dreamcatcher33/article/details/44196829</guid><author>dreamcatcher33</author><pubDate>Wed, 11 Mar 2015 11:53:12 +0800</pubDate><description><![CDATA[第一章 事件的概率
第二章 随机变量及概率分布
随机变量的函数的概率分布
正态分布
由均匀分布U(0,1)产生任意分布F(x):
第三章 随机变量的数字特征
3.1. 期望
期望的性质：(性质优良)
条件期望
3.2. 中位数 link
3.3. 方差和矩
3.4. 协方差，相关系数
3.5 累积量κn（cumulants)
3.6 大数定理/定律和中心极限定理]]></description><category></category></item></channel></rss>