Participation in Decentralized Local Governance: Two Contrasting Cases from the Philippines

Contrary to the popular assumptions among international donor agencies, preceding studies have questioned the causal relations between decentralization, participation and pro-poor policy outcomes. This article introduces two cases of decentralized city governments in the Philippines: one employs radical forms of civil participation, while the other introduces modest ones, but both of them have been successfully launching pro-poor policies. Through referring these contrasting cases to a “participatory governance” model and a “governance with trusts” model, the paper argues that the approach to local governance is not linear.

Quadratic fractional age assumption revisited

This paper introduces a quadratic fractional age assumption which makes the force of mortality and survival function continuous at all ages. The necessary and sufficient condition for the assumption to be valid is derived. Important life table parameters are estimated and applications are shown using several life table data.     

Stepwise local influence in generalized autoregressive conditional heteroskedasticity models

Detection of outliers or influential observations is an important work in statistical modeling, especially for the correlated time series data. In this paper we propose a new procedure to detect patch of influential observations in the generalized autoregressive conditional heteroskedasticity (GARCH) model. Firstly we compare the performance of innovative perturbation scheme, additive perturbation scheme and data perturbation scheme in local influence analysis. We find that the innovative perturbation scheme give better result than other two schemes although this perturbation scheme may suffer from masking effects. Then we use the stepwise local influence method under innovative perturbation scheme to detect patch of influential observations and uncover the masking effects. The simulated studies show that the new technique can successfully detect a patch of influential observations or outliers under innovative perturbation scheme. The analysis based on simulation studies and two real data sets show that the stepwise local influence method under innovative perturbation scheme is efficient for detecting multiple influential observations and dealing with masking effects in the GARCH model.     

Does location influence executive compensation? Evidence from Canadian SMEs

We examine the impact of geographic location on the level and structure of executive compensation of small and medium enterprises (SMEs) in Canada, using a panel data sample between 2008 and 2011. Our results show that SMEs pay a higher price for talent by paying a large proportion (71%) of compensation as guaranteed cash pay to their executives. We also report a strong influence of location on compensation structure. Specifically, rural firms pay 13% more incentive based equity pay to their executives compared to their size matched urban counterparts. However, there is no difference between the total compensation for managers of rural and urban firms after controlling for the cost of living index. In cross-sectional tests, we observe that total compensation is positively related to CEO/Chair duality and family ownership but is not related to management quality. In addition, we find that rural firms display a higher pay-performance sensitivity.     

Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model

This article considers the shrinkage estimation procedure in the Cox's proportional hazards regression model when it is suspected that some of the parameters may be restricted to a subspace. We have developed the statistical properties of the shrinkage estimators including asymptotic distributional biases and risks. The shrinkage estimators have much higher relative efficiency than the classical estimator, furthermore, we consider two penalty estimators—the LASSO and adaptive LASSO—and compare their relative performance with that of the shrinkage estimators numerically. A Monte Carlo simulation experiment is conducted for different combinations of irrelevant predictors and the performance of each estimator is evaluated in terms of simulated mean squared error. Simulation study shows that the shrinkage estimators are comparable to the penalty estimators when the number of irrelevant predictors in the model is relatively large. The shrinkage and penalty methods are applied to two real data sets to illustrate the usefulness of the procedures in practice.     

A class of distributions connected to order statistics with nonintegral sample size

Newton's binomial series expansion is used to develop a (class of) distribution function(s) F_r:∝ which may be interpreted as the distribution of the r^thorder statistic with nonintegral sample size∝. It is shown that F_r:∝ has properties similar to F_r:n. Multivariate extension is considered and an elementary proof of the integral representation for the joint distribution of a subset of order statistics is given. An application is included.     

Estimation of Slope for Linear Regression Model with Uncertain Prior Information and Student-t Error

This article considers estimation of the slope parameter of the linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope. Incorporating uncertain non sample prior information with the sample data the unrestricted, restricted, preliminary test, and shrinkage estimators are defined. The performances of the estimators are compared based on the criteria of unbiasedness and mean squared errors. Both analytical and graphical methods are explored. Although none of the estimators is uniformly superior to the others, if the non sample information is close to its true value, the shrinkage estimator over performs the rest of the estimators.     

A novel weighted likelihood estimation with empirical Bayes flavor

We propose a novel approach to estimation, where a set of estimators of a parameter is combined into a weighted average to produce the final estimator. The weights are chosen to be proportional to the likelihood evaluated at the estimators. We investigate the method for a set of estimators obtained by using the maximum likelihood principle applied to each individual observation. The method can be viewed as a Bayesian approach with a data-driven prior distribution. We provide several examples illustrating the new method and argue for its consistency, asymptotic normality, and efficiency. We also conduct simulation studies to assess the performance of the estimators. This straightforward methodology produces consistent estimators comparable with those obtained by the maximum likelihood method. The method also approximates the distribution of the estimator through the “posterior” distribution.     

Economic and social inclusion of person with disability and transgender through skills development program of BRAC: A qualitative study

This article is based on the Skills Training for Advancing Resources (STAR) project for the youth initiative of the Bangladesh Rural Advancement Committee (BRAC) in Bangladesh. The objective was to explore the consequences of social and economic inclusion of this project for the persons with disability (PWDs) and transgender (TG) youth people. The research focused on how inclusive programmatic interventions created multidimensional impacts among the “marginalized” and “excluded” PWD and TG youths at the grassroots level. This study adopted a qualitative approach where in-depth case interviews and observation were applied for data collection. PWD and TG graduates, master craft persons (MCPs), employers, and program staff members were the participants in this study. Results found that PWD and TG youths faced vulnerabilities and social stigma in their lives and livelihood trajectories due to their physical inability and low level of social dignity. The STAR project has a certain level of contribution to the livelihoods of PWD and TG people, where these helped them to gain their social, cultural, and economic capital. Findings would be an important guideline for policymakers, NGO managers, and human rights workers.     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.