P. Venkatesan ¹, D. Ramamurthy ², N. Sundaram ³

Affiliations
1 Department of Statistics, National Institute for Research in Tuberculosis, ICMR, Chennai – 600031, IN
2 Department of Mathematics, Sir Theagaraya College, Chennai – 600 021, IN
3 Department of Statistics, Dr. Ambedkar Government Arts College, Chennai - 600 039, IN

Abstract

The current prevalence of HIV infection and the corresponding pattern of incidence from the beginning of the epidemic to the present time are mainly estimated by means of back-calculation method. This back-calculation method reconstructs the past pattern of HIV infection and predicts the future number of AIDS cases with the present infection status. The basic data required for back-calculation methodology is the number of AIDS cases over a period of time. TANSACS publishes the reported number of AIDS cases in Tamil Nadu. In this paper, the various approaches for modeling the incubation distribution are compared using real data under various infection density distributions. The projected minimum and maximum AIDS cases in Tamil Nadu, a southern state of India, based on the reported data are 3702712 and 6936047 respectively. These estimates are based on the unadjusted AIDS incidence data. The purpose of this paper is to review the contribution of back-calculation method to our understanding of the AIDS and to summarize and interpret the epidemiological findings.

Keywords

HIV/AIDS,Incubation Period, Estimation, Infection Distributions, Back Calculation

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P. Venkatesan ¹, N. Sundaram ²

Affiliations
1 Department of Statistics, Tuberculosis Research Centre, ICMR, Chennai-600 031, Tamilnadu, IN
2 Department of Statistics, Dr.Ambedkar Government Arts College (Autonomous), Vyasarpadi, Chennai-600 039, Tamilnadu, IN

Abstract

The Exponentiated Exponential (EE) model serves as an alternative to Exponential, Weibull and Gamma models. It is observed that EE model has been used in the analysis of complete life time data. In this paper an attempt has been made to study the modeling of censored survival data and the results are compared with other models. Log Likelihood ratio statistic and Cox-Snell residuals are used for the comparisons. The EE model performs better than Exponential and Weibull models. We also fitted Log-logistic model and compared with other models based on Baysian information criterion (BIC) and an information criterion (AIC). The Log-logistic model also performs better than the above models in situations when the censoring is at low level.

Keywords

EE Model, Hazard Function, Life Time Data, Survival Function, Weibull Model

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N. Sundaram ¹, M. Sriram ¹

Affiliations
1 Department of Commerce, School of Social Sciences and Languages, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

Objective: This paper investigates the extent of financial inclusion by branchless banking technologies in Vellore district of Tamil Nadu, India. Methods/ Statistical Analysis: The respondents of this study were bank account holders who have already started to infiltrate in the financial inclusion process. The typical branchless banking technologies used in this study were such as internet banking, Automated Teller Machines (ATMs), Point-of-Sale system (POS), mobile banking and kiosk banking. To statistically prove the state of financial inclusion in the study area, Structural Equation Modeling (SEM) was used. Findings: It is resulted that there is another significant relationship identified between income and internet banking. It is concluded that income has higher influence and direct relationship with internet banking. It is suggested that there is a need of cost cut and affordable technologies to the vulnerable. It will enhance the access and use of financial system, creates job opportunities, investments in education and to manage unexpected risks and economic shocks. Application/ Improvement: This study used SEM with the support of technologies to identify the level of financial inclusion.

Keywords

Financial Inclusion, Branchless Banking Technology, Vellore District.

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