Registered Data
Contents
- 1 [CT085]
- 1.1 [02420] Nonparametric Bivariate Density Estimation for Missing Censored Lifetimes
- 1.2 [01452] Some Statistical Properties and Maximum Likelihood Estimation of Parameters of Bivariate Modified Weibull Distribution with its Real-Life Applications
- 1.3 [00098] Robust bring your own encryption algorithm using generalized heat equation associated with generalized Vigen$\grave{e}$re-type table over symmetric group
- 1.4 [02159] A method for metabolic pathway finding from gene expression data
[CT085]
[02420] Nonparametric Bivariate Density Estimation for Missing Censored Lifetimes
- Session Date & Time : 3E (Aug.23, 17:40-19:20)
- Type : Contributed Talk
- Abstract : Estimation of the joint density of two censored lifetimes is a classical problem in survival analysis, but only recently the theory and methodology of efficient nonparametric estimation have been developed. A familiar complication in survival analysis is that in real data censored lifetimes and indicators of censoring may be missing. For the model of missing completely at random, an efficient bivariate density estimator is proposed, and a practical example is presented.
- Classification : 62N02, 62G05, 62G07, Missing data, survival analysis and censoring, nonparametric estimation
- Author(s) :
- Lirit Fuksman (The University of Texas at Dallas)
[01452] Some Statistical Properties and Maximum Likelihood Estimation of Parameters of Bivariate Modified Weibull Distribution with its Real-Life Applications
- Session Date & Time : 3E (Aug.23, 17:40-19:20)
- Type : Contributed Talk
- Abstract : Real-life data sets with ties arise quite commonly in medicine, industry, reliability and survival analysis. We attempt to model such types of data sets using bivariate distributions with singular components. For this purpose, we consider mainly two types of approaches, namely the "Minimization approach" and the "Maximization approach." Using the minimization approach the bivariate modified Weibull (BMW) distribution is derived. Due to five parameters, the BMW is a more general and flexible distribution. It reduces to the Marshall-Olkin bivariate exponential (MOBE) and Marshall-Olkin bivariate Weibull (MOBW) distributions under certain parameter restrictions. Some distributional, modal and aging properties of BMW will be discussed. The copula associated with BMW distribution is given. Finally, we will discuss the maximum likelihood estimation of parameters of BMW distribution via the EM algorithm. We will give some numerical results of a real-life data set with ties.
- Classification : 62Nxx, Mainly to developed models to analyze real life bivariate data sets where the ties occur naturally in the data sets. The data may be censored . Such type of models are known as Bivariate distributions with singular component.
- Author(s) :
- Sanjay Kumar (Ph.D. Student, Department of Mathematics & Statistics, Indian Institute of Technology Kanpur)
- Debasis Kundu (Professor, Department of Mathematics & Statistics, Indian Institute of Technology Kanpur)
- Sharmishtha Mitra (Professor, Department of Mathematics & Statistics, Indian Institute of Technology Kanpur)
[00098] Robust bring your own encryption algorithm using generalized heat equation associated with generalized Vigen$\grave{e}$re-type table over symmetric group
- Session Date & Time : 3E (Aug.23, 17:40-19:20)
- Type : Contributed Talk
- Abstract : We develop a secure bring your own encryption algorithm that encrypts personal data. The proposed algorithm is based on a generalized heat equation associated with a generalized Vigen$\grave{e}$re-type table over symmetric group $S_{n}$ so that existing attacks will be infeasible. Encryption keys are obtained from random key sequences tested by NIST statistical test suite. The robustness of the proposed algorithm has been found by comparing it with other competing existing algorithms.
- Classification : 68P25, 68P30, Image encryption.
- Author(s) :
- Manish Kumar (BITS Pilani, Hyderabad Campus, Telangana, India)
[02159] A method for metabolic pathway finding from gene expression data
- Session Date & Time : 3E (Aug.23, 17:40-19:20)
- Type : Industrial Contributed Talk
- Abstract : We introduce a pathway search method based on weighted reactant–product pairs with carbon atom tracing, using gene expression data in combination with a shortest path graph search. Enzymatic reactions are classified into highly, moderately and lowly expressed depending on gene expression data, and edges weights are assigned accordingly, letting us compare different biological states, e.g., treated vs non-treated patients. As a result, we provide those paths which are significantly enriched contextualized to given biological states.
- Classification : 68R10, 92D10
- Author(s) :
- Damian Alejandro Knopoff (CONICET, Argentina & Intelligent Biodata SL, Spain)
- Jon Pey (Intelligent Biodata SL)