Volume 1 Issue 1
A Parametric Approach for Estimation of Technical efficiency of Carp Culture Farms of Kolleru Lake, Andhra Pradesh, India
Ajit Kumar Roy*
A stochastic frontier production function model was utilized to estimate the technical efficiency of carp farms operating in Kolleru Lake area that is the largest freshwater wetland ecosystem with shallow water area covering 955 sq. km upto 10.7’ contour located in between Krishna and Godavari delta of southern India. Analysis was based on sample of 221 carp farms. The estimated mean technical efficiency (TE) was found to be 0.7260. The highest significant elasticity of coefficient was observed for feed (0.2001) followed by organic manure (0.1411) justifying the importance of these two inputs in yield of carps.
On Weighted Lindley Distribution and its Applications to Model Lifetime Data
Rama Shanker*, Kamlesh Kumar Shukla, Hagos Fesshaye
In the present paper, the expressions for coefficient of variation, skewness, kurtosis and index of dispersion of weighted Lindley distribution (WLD), of which Lindley distribution is a particular case, have been derived. The nature and behavior of coefficient of variation, skewness, kurtosis, and index of dispersion of WLD have also been discussed for varying values of its parameters. The stochastic ordering of the distribution has been studied. The applications and goodness of fit of the distribution have been discussed with several lifetime data sets and the fit has been compared with one parameter Lindley and exponential distributions.
Entropy of Selection Procedures for Unequal Probability Sampling
Anam Riaz , Abdul Basit*, Zafar Iqbal and Munir Ahmad
Entropy measure has been used to compare the different selection procedures for unequal probability sampling. Basit and Shahbaz derived the general class of selection procedures for sample size two and sample size ‘n’. To compare the selection procedures, Shannon entropy has been used for these selection procedures. This study claims that selection procedure with a higher entropy will produce the smaller variance of Horvitz - Thompson estimator and as well as variance of Murthy estimator.
Multiple Tests in Group Sequential Clinical Trials
Zhao T., Baron M.∗
Efficient methods are elaborated for the simultaneous testing of multiple hypotheses in group sequential clinical trials. Proposed tests control the Type I and Type II familywise error rates at the levels of α and β and require sampling at most K groups of patients, where α, β, and K are pre-assigned. The new step-down sequential technique allow to reduce the overall sample size under these constraints. It results in a substantial cost saving over the Bonferroni-corrected Pocock and O’Brien-Fleming tests. Optimization of the truncated single-hypothesis sequential probability ratio test appears more efficient than the Pollock-Golhar sequential rule proposed earlier for the same problem.r.
The Discrete Poisson-Shanker Distribution
The Shanker distribution defined by its probability density function (p.d.f.) and corresponding cumulative distribution function (c.d.f) has been introduced by Shanker for modeling real lifetime data-set from engineering and biomedical science.
An Alternative Perspective on Consensus Priors with Applications to Phase I Clinical Trials
Steven B. Kim*, Daniel L. Gillen
We occasionally need to make a decision or a series of decisions based on a small sample. In some cases, an investigator is knowledgeable about a parameter of interest in some degrees or is accessible to various sources of prior information. Yet, two or more experts cannot have an identical prior distribution for the parameter. In this manuscript, we discuss the use of a consensus prior and compare two classes of Bayes estimators.
A Study of Statistical and Machine Learning Methods for Cancer Classification Using Cross-Species Genomic Data
Cuilan Gao*, Behrouz Shamsaei, Stan Pounds
Use of gene expression profiling of animal model of a certain disease gives pre-clinical insights for the potential efficacy of novel treatments and drugs. Selection of an animal model, accurately resembling the human disease, profoundly reduces the research cost in resources and time. In this paper, we introduce and compare three different methods for classification of sub-types of cancer via cross-species genomic data. A statistical procedure based on analysis of variance (ANOVA) of similarity of gene expression between human and animal is used to select the animal model that most accurately mimics the human disease.