How the acculturation of Baba Nyonya community affects Malacca food identity?

ABSTRACT

The migration of the Chinese community to Malacca in the 16th century has enabled the process of cross culturing and culinary acculturation, producing a unique mixture of Baba Nyonya food. Even though Baba Nyonya food is pretty much has been localised (Malay), its Chinese representation cannot be denied. This study aims to assess how the acculturation of the Baba Nyonya community affects Malacca food identity. A quantitative methodology is employed in this study. This study classifies Baba Nyonya food acculturation through (1) types of food; (2) methods of cooking; (3) ingredients; and (4) eating decorum. From the regression analysis, this study found that Baba Nyonya cultural polarity explains significant variation in the formation of Malacca food identity. This study then elaborates on the importance of Baba Nyonya food in Malacca destination branding.     

Correction to: Patronage and Public Goods Provisioning in an Unequal Land

Social Indicators Research - The article Patronage and Public Goods Provisioning in an Unequal Land, written by Hadia Majid and Rashid Memon, was originally published electronically on the...     

Determinants of Infant Mortality in Older ASEAN Economies

Infant mortality in the Association of Southeast Asian Nations (ASEAN) has been declining, yet disparities remain between the nations. This paper therefore explores the determinants of infant mortality in the older ASEAN-4 economies, Malaysia, Thailand, Indonesia and the Philippines using an Autoregressive Distributed Lag (ARDL) Error Correction Model framework. The key findings of the study are: First, there is evidence of long-run relationships among infant mortality, education, female fertility, income and access to healthcare. Second, the determinants of infant mortality vary between countries. Female fertility emerged as the main determinant of infant mortality in Malaysia, while access to healthcare matter for infant mortality in Indonesia, and to a lesser extent for the Philippines. The income effect is significant for reducing infant mortality in Malaysia, while female education is important for Indonesia and Thailand. Third, the speed of adjustment of infant mortality rate is comparatively low in ASEAN-4.     

Semi-doubly optimal concentric circles fitting with presence of heteroscedasticity

Previous studies focus on homogeneous and isotropic assumptions about the noisy data. Many methods have been developed recently for fitting concentric circles to data. In this paper, these statistical assumptions have been relaxed. To the best of our knowledge, only one iterative method has been recently developed. Due to its complexity, no such algorithm is available to compute the reliable maximum likelihood estimator (MLE). Accordingly, we have developed four new methods that outperform the existing methods including the orthogonal distance regression (ODR). We also discuss which of these methods is superior according to the four principles: statistical efficiency, accuracy, robustness, and computational efficiency. Numerical experiments on synthetic and real images have been conducted to validate our findings.     

On the $$L_p$$ norms of kernel regression estimators for incomplete data with applications to classification

We consider kernel methods to construct nonparametric estimators of a regression function based on incomplete data. To tackle the presence of incomplete covariates, we employ Horvitz–Thompson-type inverse weighting techniques, where the weights are the selection probabilities. The unknown selection probabilities are themselves estimated using (1) kernel regression, when the functional form of these probabilities are completely unknown, and (2) the least-squares method, when the selection probabilities belong to a known class of candidate functions. To assess the overall performance of the proposed estimators, we establish exponential upper bounds on the \(L_p\) norms, \(1\le p<\infty \), of our estimators; these bounds immediately yield various strong convergence results. We also apply our results to deal with the important problem of statistical classification with partially observed covariates.     

A note on the mean residual life of a coherent system with a cold standby component

In this paper, we investigate the effect of a cold standby component on the mean residual life (MRL) of a system. When the system fails, a cold standby component is immediately put in operation. We particularly focus on the coherent systems in which, after putting the standby component into operation, the failure of the system is due to the next component failure. For these systems, we define MRL functions and obtain their explicit expressions. Also some stochastic ordering results are provided. Such systems include k-out-of-n systems. Hence, our results extend some results in literature.     

Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3–5-year-old children

The autologistic model, first introduced by Besag, is a popular tool for analyzing binary data in spatial lattices. However, no investigation was found to consider modeling of binary data clustered in uncorrelated lattices. Owing to spatial dependency of responses, the exact likelihood estimation of parameters is not possible. For circumventing this difficulty, many studies have been designed to approximate the likelihood and the related partition function of the model. So, the traditional and Bayesian estimation methods based on the likelihood function are often time-consuming and require heavy computations and recursive techniques. Some investigators have introduced and implemented data augmentation and latent variable model to reduce computational complications in parameter estimation. In this work, the spatially correlated binary data distributed in uncorrelated lattices were modeled using autologistic regression, a Bayesian inference was developed with contribution of data augmentation and the proposed models were applied to caries experiences of deciduous dents.     

Empirical Bayes spatial prediction using a Monte Carlo EM algorithm

This paper deals with an empirical Bayes approach for spatial prediction of a Gaussian random field. In fact, we estimate the hyperparameters of the prior distribution by using the maximum likelihood method. In order to maximize the marginal distribution of the data, the EM algorithm is used. Since this algorithm requires the evaluation of analytically intractable and high dimensionally integrals, a Monte Carlo method based on discretizing parameter space, is proposed to estimate the relevant integrals. Then, the approach is illustrated by its application to a spatial data set. Finally, we compare the predictive performance of this approach with the reference prior method.     

Deconstruction,postmodernism and philosophy of science: Some Epistemo‐critical bearings

Abstract

In the recent critique of ‘Western metaphysics’ by post‐structuralist and postmodern theorists, there has emerged a distinctive line of thought which seeks to apply such critique to the domain of political theory. This paper approaches Hannah Arendt's conceptualisation of the political as a proto‐type of such a theorisation, deploying as it does key elements of the Heideggerian (and more broadly, phenomenological) position so as to rethink the nature of the political. By delineating the specifically ‘post‐metaphysical’ moments of Arendt's theory and its corresponding critique of political modernity, I endeavour to illuminate both the advantages and pitfalls of contemporary efforts at developing a philosophical conception of the political on the basis of a neo‐Heideggerian position.