幸福=？

在@新浪财经举办的#金嘏麟论坛#上。全球领先的市场研究集团@Ipsos益普索市场研究联合新浪在多年长期合作的基础上发布了的《幸福的成本微博白皮书》解密幸福的公式：幸福=f（财富基础、社会保障、感情归属、普世价值观、发展空间）。众多媒体报道了现场白皮书发布盛况：幸福的关键是什么？为了幸福要付出什么？千万网友参与#幸福的成本#大调查，晒“幸福的成本”，住房、医疗、教育、养老成最热幸福“关键词”，是新时代最影响幸福感的“四座大山”。     

开发商建“廉租房”=社会责任？

2006年国家针对房地产发展的宏观调控文件可谓密集,从温家宝总理提出“国六条”到九部委“国十五条”,再到新近的“十八条”,宏观调控从上而下,调控环节牵涉到目前产业和市场发展的方方面面。开发商作为产业和市场运行的主体,自然受各项政策的多方面影响。每一条细项,无不牵动企业家绷紧的神经。无论如何,开发商在我国房地产市场发展过程中起到了举足轻重的作用,对于改善居民居住环境和居住水平可谓功不可没,政策调控对开发商主要起引导与规范作用。但开发商利润超出全社会平均利润水平是不争的事实,因此也受到了来自民众的非议。事实就是如…     

关于猜想n=N^2＋P的研究

(一)猜想n=N2+P著名数字家哈代(G·H·H ardy)和李特伍德(J·E·littlew ood)1922年曾猜想:(1)每一个充分大的不是完全平方的自然数可表示成一个素数与一个自然数的平方和,记为n=N2+P;(2)每一个充分大的整数都可以表示为一个素数和两个整数的平方和,记为n=x2+y2+p。这两个猜想统称为哈代和李特伍德猜想,其中第二个猜想1960年已被解决,但第一个猜想在此之前始终未曾解决,本文尝试在这一问题上进行探讨。(二)引例把自然数的平方数依次列出,并对应地列出相邻二数之差:149162536496481100…(1)35791113151719…(2)例如给自然数108由(1)知道1…     

选择了一个好课题=就等于成功了一半

选题是撰写统计分析文章的第一步,选好了课题,也就确定了研究对象.选题的过程就是研究选题对象的过程,就是确定分析研究对象的范围,确定分析的主题思想,规划文章基本内容的过程.选题恰当可以提高统计分析文章的价值,选题得当可以使作者比较自如地围绕主题提出自己的观点和论据,比较顺利地选择材料进行加工,进而考虑内容的安排和叙述的层次,也较容易提高分析的深度.“选择了一个好课题就等于成功了一半”.因此我们要重视选题工作,要舍得花时间去研究选题的内容.课题要选择领导关心的热点、要点;选择社会经济活动、企业改革中的新情况、新问     

Regular A-optimal design matrices X = (xij) with xij = ?1, 0, 1

New construction methods of the regular A-optimal design matrices with elements −1, 0, 1 are presented, under assumption of nonhomogeneity of variance error. The presented constructions are based on the incidence matrices of the balanced bipartite weighing designs.     

Inference of δ = P(X < Y) for Burr XII distributions with record samples

Let X and Y follow independent Burr type XII distributions, which share a common inner shape parameter. The maximum likelihood estimator of the parameter δ = P(X < Y) is studied based on record samples. The existence and uniqueness of the maximum likelihood estimator of δ based on record samples are established. When the inner shape parameter is known, an exact confidence interval of δ is derived; otherwise, the Fisher information matrix and two bootstrap methods are used to obtain three approximate confidence intervals of δ. The performances of the proposed methods are evaluated via Monte Carlo simulation. Two examples are provided for illustration.     

Estimation of δ=P(X<Y) for Burr XII distribution based on the progressively first failure-censored samples

Let X and Y have two-parameter Burr XII distributions. The maximum-likelihood estimator of δ=P(X<Y) is studied under the progressively first failure-censored samples. Three confidence intervals of δ are constructed by using an asymptotic distribution of the maximum-likelihood estimator of δ and two bootstrapping procedures, respectively. Some computational results from intensive simulations are presented. An illustrative example is provided to demonstrate the application of the proposed method.     

A Note on Overdifferencing and the Equivalence of Seasonal Time Series Models With Monthly Means and Models With (0, 1, 1)12 Seasonal Parts When ⊖ = 1

Two general models for monthly seasonal time series are considered, one in which seasonality is modeled with monthly means and another in which seasonality is modeled with a (0, 1, 1)₁₂ ARIMA structure. The models are shown to be equivalent if the seasonal moving average parameter (?) is 1 and if the same assumptions about the 12 initial observations are made for both models. The role of the assumptions about the initial observations is analyzed, and it is argued that for practical purposes the two models can be regarded as equivalent when ? = 1. It is observed that the result extends easily to more general models involving overdifferencing.     

Small -sample comparison of the exact and symptotic upper tail probabilitiesof chi-squared goodness -of-fit statistics: pearson's x 2,likelihood ratio,and power-divergence statistic(λ = 2/3)

The exact and asymptotic upper tail probabilities ( α= .l0, .05, .01, .001) of the three chi-squared goodness-of-fit statistics Pearson's X ², likelihood ratioG ², and power-divergence statisticD ² (λ ) , with λ = 2/3, are compared numerically for simple null hypotheses not involving parameter estimation. Three types of such hypotheses were investigated (equal cell probabilities, proportional cell probabilities, some fixed small expectations together with some increasing large expectations) for the number of cells being between 3 and 15, and for sample sizes from 10 to 40, increasing by steps of one. Rating the relative accuracy of the chi-squared approximation in terms of ±10% and ±20% intervals around α led to the following conclusions: 1. Using G ² is not recommended. 2 . At the more relevant significance levels α = .10 and α = .05X ² should be preferred over D ². Solely in case of unequal cell probabilitiesD ² is the better choice at α = .O1 and α = .001. 3 . Yarnold's (1970; Journal of the Amerin Statistical Association, 65, 864-886) rule for the minimum expectation when using X ² ("If the number of cells k is 3 or more, and if r denotes the number of expectations less than 5, then the minimum expectation may be as small as 5r/k.") generalizes to D ²; it gives a good lower limit for the expected cell frequencies, however, when the number of cells is greater than 3. For k = 3 , even sample sizes over 15 may be insufficient.