An Extension of the Geometric Distribution with Properties and Applications

A new parameter is introduced to extend the geometric distribution using Azzalini's method. Several important structural properties of the proposed two-parameter extended geometric distribution are investigated. Characterizations including for the geometric distribution, in terms of the proposed model, are established. Maximum likelihood estimation, method of moment estimation and relative frequency based estimation of the parameters are discussed in detail. The likelihood ratio test regarding relevance of the additional parameter is presented. Bayesian estimation of the parameters using STAN is also discussed. The proposed model is compared with some recently introduced two-parameter count models by analyzing two real-life datasets. The findings clearly indicate superiority of the proposed model over the rest

Discovering potential blood-based cytokine biomarkers for Alzheimer’s disease using Firth Logistic Regression

Background: Alzheimer’s disease (AD) is a neurodegenerative disorder where patients suffer from memory loss, cognitive impairment and progressive disability. Individual blood biomarkers have not been successful in defining the disease pathology, progression and diagnosis of AD. There is a need to identify multiplex panels of blood biomarkers for early diagnosis of AD with high sensitivity and specificity. This study focused on identification of cytokine biomarkers. The maximum likelihood estimates of the ordinary logistic regression model cannot be obtained when there is complete separation and the alternative is Firth logistic regression which uses a penalised Maximum Likelihood in parameter estimation.  Methods: This paper reports a Firth logistic regression application in finding potential blood-based cytokine biomarkers for Alzheimer’s disease in a matched case control study. We used a principle component analysis to discriminate the correlated, completely separated covariates.  Results: The Firth logistic regression results showed that nine individual biomarkers IL-1β, IL-6, IL-12, IFN-γ, IL-10, IL-13, IP-10, MCP-1 and MIP-1α had a significant relationshipwith elevated risk for AD as compared to the healthy control (HC). Principal component analysis with varimax rotation for the nine biomarkers revealed four factors (total variance explained=85.5%). The main principal component biomarkers were IL-1β, IL-6, IL-13 and MCP-1 (total variance explained=62.3%). Firth’s logistic regression model with the first principal component had accuracy of 78.2% with sensitivity and specificity of 71.8% and 75% respectively.  Conclusion: Firth’s logistic regression is a useful technique in identification of significant biomarkers when there is an issue of data separation.&nbsp

An alternative hyper-Poisson integer-valued GARCH model with application to polio, internet protocol and COVID-19 data

Time series of counts are observed widely in actuarial science, finance, epidemiology and biology. These time series may exhibit over-, equi- and under-dispersion. The Poisson distribution is commonly used in count time series models, but it is restricted by the equality of mean and variance. Other distributions such as the generalized Poisson, double Poisson, hyper-Poisson, and COM-Poisson distributions have been proposed to replace the Poisson distribution to model the different levels of dispersion in time series of counts. These models have certain limitations such as complex expressions for the mean and variance which complicate the formulation as GARCH models. In this study, we propose an alternative hyper-Poisson (AHP) distribution, with simple forms of conditional mean and variance, for an integer-valued GARCH (INGARCH) model for time series of counts that also exhibit the different levels of dispersion. We demonstrate that the AHP-INGARCH model is comparable to some existing INGARCH models. Additionally, the model can cover a wider range of dispersion. The maximum likelihood estimation can be used to estimate the parameters of the proposed model. Applications to three real-life data sets related to polio, internet protocol and daily COVID-19 new deaths underscore the usefulness of the proposed model in studying both over-dispersed and under-dispersed time series of counts

Interesterified palm olein (IEPalm) and interesterified stearic acid-rich fat blend (IEStear) have no adverse effects on insulin resistance: a randomized control trial

Chemically-interesterified (CIE) fats are trans-fat free and are increasingly being used as an alternative to hydrogenated oils for food manufacturing industries to optimize their products’ characteristics and nutrient compositions. The metabolic effects of CIE fats on insulin activity, lipids, and adiposity in humans are not well established. We investigated the effects of CIE fats rich in palmitic (C16:0, IEPalm) and stearic (C18:0, IEStear) acids on insulin resistance, serum lipids, apolipoprotein concentrations, and adiposity, using C16:0-rich natural palm olein (NatPO) as the control. We designed a parallel, double-blind clinical trial. Three test fats were used to prepare daily snacks for consumption with a standard background diet over a period of 8 weeks by three groups of a total of 85 healthy, overweight adult volunteers. We measured the outcome variables at weeks 0, 6, and at the endpoint of 8. After 8 weeks, there was no significant difference in surrogate biomarkers of insulin resistance in any of the IE fat diets (IEPalm and IEStear) compared to the NatPO diet. The change in serum triacylglycerol concentrations was significantly lower with the IEStear diet, and the changes in serum leptin and body fat percentages were significantly lower in the NatPO-diet compared to the IEPalm diet. We conclude that diets containing C16:0 and C18:0-rich CIE fats do not affect markers of insulin resistance compared to a natural C16:0-rich fat (NatPO) diet. Higher amounts of saturated fatty acids (SFAs) and longer chain SFAs situated at the sn-1,3 position of the triacylglycerol (TAG) backbones resulted in less weight gain and lower changes in body fat percentage and leptin concentration to those observed in NatPO and IEStear

A class of bivariate negative binomial distributions with different index parameters in the marginals

In this paper, we consider a new class of bivariate negative binomial distributions having marginal distributions with different index parameters. This feature is useful in statistical modelling and simulation studies, where different marginal distributions and a specified correlation are required. This feature also makes it more flexible than the existing bivariate generalizations of the negative binomial distribution, which have a common index parameter in the marginal distributions. Various interesting properties, such as canonical expansions and quadrant dependence, are obtained. Potential application of the proposed class of bivariate negative binomial distributions, as a bivariate mixed Poisson distribution, and computer generation of samples are examined. Numerical examples as well as goodness-of-fit to simulated and real data are also given here in order to illustrate the application of this family of bivariate negative binomial distributions. (C) 2010 Elsevier Inc. All rights reserved

[[alternative]]Malaysia Reinterpret Version of Combined Belief in Zhou Dynasty's Zhongliu Deity (Lord of Household Area) and Taoistic Five Dragons for Land Stability which was Evoluted from Southern China

[[abstract]]在馬來西亞以及鄰近地區華人社會，「五方五土龍神，唐番地主財神」可說是當地華人信仰體系裡頭最為基層的崇祀對象，常見於家家戶戶。可是，嚴格來說，「五方五土龍神，唐番地主財神」作為保障家居環境的守護神靈，並非單數的地祗崇拜，而是結合著「五方龍神」與「本宅土地」兩大土地神靈體系，集中崇拜五位龍神以及唐、番兩位地主財神，成為南洋特有的信仰現象。南洋華人先民大多源自華南各地，本應深受原籍地之信仰文化薰陶；「五方龍神，唐番地主」三結合的信仰現象能在南洋流傳超過百年，信眾猶能相安無事，可知其最初之存在絕非單純源於民間信仰之隨機組合。反之，如果從閱讀歷代文獻對照其中信仰現象，又可發現「五方五土，唐番地主」之信仰內涵最早源自於古代儒學經籍有關「中霤」與「化厲為神」的神道設教，並且接納了道教自南北朝以來對「五方龍神」之神學建構；到了南洋華人拓殖往昔中華南海諸藩的過程，則又進一步詮釋中華中霤神祀結合居住地先靈崇拜的演變，滲入了當地對土地神靈的設想，認為感恩先前開拓其居住範圍的先人，必定包括原來落地生根的「唐、番」兩族，兼對「中華」與「地方」雙構安居樂業因素的神聖象徵崇德報功。原有的「五方五土龍神，唐番地主財神」是繼承著上述儒道論述相融於華南民間的信仰實踐，面向南洋本土脈絡再次演變成為地方信仰。[[abstract]]The worshiping of the Collective Land Deities who are collectively named as “Wu Fang Wu Tu Long Shen, Tang Fan Di Zhu Cai Shen” on a piece of landed Tablet is really common among the Chinese families in Malaysia and neighboring region. Anyway, such combination of beliefs is not a disarray random arrangement. The twelve words written in the form of Chinese calligraphy are actually meant it is a combined belief of Zhou Dynasty’s Zhongliu Deity (Lord of Household Area) and Taoistic Five Dragons for Land Stability. While the belief of “Di Zhu cai shen” (deity of household area who serve as spirit for wealthy) could referred it evolement from confucius doctrine such as “Li Ji” (Book of Rites), the teaching of “Wu Fang Wu Tu Long Shen” or “Protective Dragons of the Spirit of Five Elements for Land Balance” could trace it authenticity as part of Taoism theology by refer to “Dao Zang” (Collections of Taoism Doctrines).Anyway, by declared the local “Di Zhu” co-exciting as “Tang” (Chinese) and “Fan” (Non-Chinese), the way of description shown that the Chinese collectively aware that their local achievement are based on the contributions of former generations no matter they are Chinese or natives and the sense of gratitude must be performed in a proper way that treat the all the “Di Zhu” as a spiritual elders of the family. As conclusion, the Nanyang Chinese worshiping of “Wu Fang Wu Tu Long Shen, Tang Fan Di Zhu Cai Shen” is a inheritance of both the tradition of Confucianism and Taoism that have a long history of practice in Southern China, but sustain and strengthen it tradition by evolute and localize according to the recognition and absorbing of vernacular context

Mining dependency relations for query expansion in passage retrieval

Classical query expansion techniques such as the local context analysis (LCA) make use of term co-occurrence statistics to incorporate additional contextual terms for enhancing passage retrieval. However, relevant contextual terms do not always cooccur frequently with the query terms and vice versa. Hence the use of such methods often brings in noise, which leads to reduced precision. Previous studies have demonstrated the importance of relationship analysis for natural language queries in passage retrieval. However, they found that without query expansion, the performance is not satisfactory for short queries. In this paper, we present two novel query expansion techniques that make use of dependency relation analysis to extract contextual terms and relations from external corpuses. The techniques are used to enhance the performance of density based and relation based passage retrieval frameworks respectively. We compare the performance of the resulting systems with LCA in a density based passage retrieval system (DBS) and a relation based system without any query expansion (RBS) using the factoid questions from the TREC-12 QA task. The results show that in terms of MRR scores, our relation based term expansion method with DBS outperforms the LCA by 9.81%, while our relation expansion method outperforms RBS by 17.49%

Close-up performance comparison of <i>sailboat</i> image.

<p>(a) Ground truth (SSI = 1), (b) FlexClulstB, 8x8 blocks (SSI = 0.8687), (c) FlexClust, 16x16 blocks (SSI = 0.8862), (d) sufficient EM, 8x8 blocks (SSI = 0.7115), and (e) sufficient EM, 16x16 blocks (SSI = 0.5506).</p

Efficient Translation, Rotation, and Scale Invariants of Discrete Tchebichef Moments

Translation rotation and scale invariants of Tchebichef moments are commonly used descriptors in image analysis. Existing invariant algorithms either indirectly compute from geometric moments or directly using Tchebichef moments. The former approach is relatively simple, but inefficient, especially when the system consists only of Tchebichef moments. Likewise, the latter approach is complicated, mainly because of the method used to formulate the invariant algorithm. Hence, in this paper, we introduce a new set of translation, rotation and scale Tchebichef moment invariants (TRSI) using moment normalization, which is much computationally efficient and accurate. This is achieved by formulating the recurrence relationship of the descriptors and successfully resolve uniqueness issues of principal axis normalization. Experimental studies show that the proposed method is computationally much faster and possesses higher discriminative power in classification when compared with present invariant algorithms. The main contribution of this paper is a novel fast computational algorithm that simplifies translation, rotation and scale invariant algorithms of Tchebichef moments and a novel normalization scheme that preserve invariants’ orthogonality from the moment functions. The technique can be deployed to derive affine invariants of Tchebichef moments, and invariants for other orthogonal moments like Krawtchouk, Hahn, Racah moments etc

Impact of population ageing on economic growth

Based on United Nation projections, world population structure have tendency to move toward the population aging structure. Population aging is said would induce several problems to a country in term of theirs macroeconomic, labour market and social security. However, researcher believed impact of aging society may vary depend on economic policies implemented. Effective policies implemented can help in offsetting the effect of population aging. Most of the early studied have utilized panel data in their analysis. Nevertheless, the results can only provide a general conclusion. In addition, previous empirical result uncovers the possibility of the existence of nonlinear relationship. Hence, both linear and nonlinear causality test are employed in this thesis to examine the causal relationship among old-age dependency ratio and work to retirement ratio on economic growth. The countries involved in this analysis include Japan, Germany, China, Malaysia and Singapore. Findings suggested that economic policies implemented do affect the impact of the population aging. Japan seems to have better policies in dealing with the aging society. Result also show that adjustment on retirement age gives significant effect on economic growth. Besides, few cases show the existence of nonlinear causal relationship. Therefore, nonlinear relationship should also be considered to avoid bias result