Incidence of Social Accountability in Local Governance: The Case of the Network for Fair,Democratic and Sustainable Cities and Territories in Latin America

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - In Latin America, the emergence of a series of civil society initiatives aimed at citizen engagement for social...     

高速铁路与经济增长的因果关系

针对中国高速铁路供给对经济增长和产业结构的影响,以及产业结构、城镇化率、人口密度如何决定高速铁路供给等问题,对高速铁路与经济增长的即时效应、快速效应和慢速效应机理进行分析。研究认为,从高速铁路对经济增长角度来看,2005~2014年中国省域面板数据实证表明:高速铁路在十年间的即时效应和快速效应不断降低,高速铁路的高时间价值与高出行成本特性主导的慢速效应占据支配地位,导致高速铁路对全国和中部地区经济增长没有显著影响,对东部地区有显著正向影响,对西部地区有显著负向影响,说明总体上高速铁路并不是经济增长的原因;从经济增长对高速铁路角度来看,Logit模型计量经济增长对高速铁路触发的影响表明:经济增长本源性需求内生出对高速铁路的即时效应、快速效应和慢速效应的派生性需求,经济增长以及第三产业、城镇化率、人口密度变量显著决定高速铁路供给。     

Estimation of covariance functions by a fully data-driven model selection procedure and its application to Kriging spatial interpolation of real rainfall data

In this paper, we propose a data-driven model selection approach for the nonparametric estimation of covariance functions under very general moments assumptions on the stochastic process. Observing i.i.d replications of the process at fixed observation points, we select the best estimator among a set of candidates using a penalized least squares estimation procedure with a fully data-driven penalty function, extending the work in Bigot et al. (Electron J Stat 4:822–855, 2010). We then provide a practical application of this estimate for a Kriging interpolation procedure to forecast rainfall data.     

Standard statistical tools for the breed allocation problem

Modern technologies are frequently used in order to deal with new genomic problems. For instance, the STRUCTURE software is usually employed for breed assignment based on genetic information. However, standard statistical techniques offer a number of valuable tools which can be successfully used for dealing with most problems. In this paper, we investigated the capability of microsatellite markers for individual identification and their potential use for breed assignment of individuals in seventy Lidia breed lines and breeders. Traditional binomial logistic regression is applied to each line and used to assign one individual to a particular line. In addition, the area under receiver operating curve (AUC) criterion is used to measure the capability of the microsatellite-based models to separate the groups. This method allows us to identify which microsatellite loci are related to each line. Overall, only one subject was misclassified or a 99.94% correct allocation. The minimum observed AUC was 0.986 with an average of 0.997. These results suggest that our method is competitive for animal allocation and has some interpretative advantages and a strong relationship with methods based on SNPs and related techniques.     

Structured orthogonal families of one and two strata prime basis factorial models

The models in structured families correspond to the treatments of a fixed effects base design \(\pi \) . Then the action of factors in \(\pi \) , on the fixed effects parameters of the models, is studied. Analyzing such a families enables the study of the action of nesting factors on the effects and interactions of nested factors. When \(\pi \) has an orthogonal structure, the family of models is said to be orthogonal. The models in the family can have one, two or more strata. Models with more than one stratum are obtained through nesting of one stratum models. A general treatment of the case in which the base design has orthogonal structure is presented and a special emphasis is given to the families of prime basis factorials models. These last models are, as it is well known, widely used in fertilization trials.     

Interpolation,outliers mid inverse autocorrelations

The paper addresses the problem of estimating missing observations in an infinite realization of a linear, possibly nonstationary, stochastic processes when the model is known. The general case of any possible distribution of missing observations in the time series is considered, and analytical expressions for the optimal estimators and their associated mean squared errors are obtained. These expressions involve solely the elements of the inverse or dual autocorrelation function of the series.

This optimal estimator -the conditional expectation of the missing observations given the available ones- is equal to the estimator that results from filling the missing values in the series with arbitrary numbers, treating these numbers as additive outliers, and removing with intervention analysis the outlier effects from the invented numbers.     

Approximations for a fixed sample size selection procedure for negative binomial populations

A large sample approximation of the least favorable configuration for a fixed sample size selection procedure for negative binomial populations is proposed. A normal approximation of the selection procedure is also presented. Optimal sample sizes required to be drawn from each population and the bounds for the sample sizes are tabulated. Sample sizes obtained using the approximate least favorable configuration are compared with those obtained using the exact least favorable configuration. Alternate form of the normal approximation to the probability of correct selection is also presented. The relation between the required sample size and the number of populations involved is studied.     

Influence Analysis in Principal Component Analysis Through Power-Series Expansions

ABSTRACT

In influence analysis several problems arise in the field of Principal Components when applying different sample versions. Among these are the difficulty of determining a certain correspondence between the eigenvalues before and after the deletion of observations, the choice of the sign of the eigenvectors and the computational problem derived from the resolution of a great number of eigenproblems. In this article, such problems are discussed from the joint influence point of view and a solution is proposed by using approximations. Furthermore, the influence on a new parameter of interest is introduced: the proportion of variance explained by a set of principal components.     

Mean Estimation Under Successive Sampling with Calibration Estimators

The estimation of the finite population mean in successive occasions is investigated with calibration estimators in this article. We propose several estimators based on calibration techniques with arbitrary sampling design in each of the occasions. Asymptotic variance formulaes are derived for the proposed estimators. The properties of these estimators are studied via a simulation study and using natural populations.