Manoj Chacko

Estimation of a Parameter of Bivariate Pareto Distribution by Ranked Set Sampling

Abstract Ranked set sampling is applicable whenever ranking of a set of sampling units can be done easily by a judgement method or based on the measurement of an auxiliary variable on the units selected. In this work, we derive different estimators of a parameter associated with the distribution of the study variate Y, based on a ranked-set sample obtained by using an auxiliary variable X correlated with Y for ranking the sample units, when (X, Y) follows a bivariate Pareto distribution. Efficiency comparisons among these estimators are also made. Real-life data have been used to illustrate the application of the results obtained.

Computation of physical characteristics of a lake system using IRS P6 (LISS-III) imagery

Abstract Lakes are versatile ecosystems and they are under the threat of eutrophication and siltation. The physical characteristics of a lake provide some insight into the status of the lake. Satellite imagery analysis now plays a prominent role in the quick assessment of characteristics of a lake system in a vast area. This study is an attempt to assess the water temperature, depth, and turbidity level of a lake system (Akkulam–Veli lake, Kerala, India) using IRS P6–LISS-III imagery. Field data were collected on the date of the overpass of the satellite. For the assessment of water temperature from satellite imagery, regression equation using spectral ratio (green/red bands) is found to yield superior results than the simple regression equation and multiple regression equation. For predicting the water depth, radiance in green and red bands can be used whereas that for turbidity, radiance in green and SWIR can be used. IRS P6–LISS-III imagery can be effectively used for the assessment of the physical characteristics of a lake system at a low cost.

Estimation of Parameters of Uniform Distribution Based on K-Record Values

Abtsrcat There are many situations where experimental outcomes are a sequence of record-breaking observations. In this paper, an extension of record models, well known as k-records, is considered. The best linear unbiased estimators (BLUEs) for the location and scale parameters of the uniform distribution are derived using k-record values. In addition, best linear invariant estimators of two parameter uniform distribution are obtained on the basis of k-record values. We have also determined the e�ciency of BLUEs relative to maximum likelihood estimators (adjusted for bias). The best linear unbiased predictor of future k-record values is also determined.

Applications of Ranked Set Sampling in Estimating Parameters of Morgenstern Type Bivariate Logistic Distribution

Abtsrcat In this work we apply ranked set sampling and its modi-fied version called extreme ranked set sampling to estimate the parameters of Morgenstern type bivariate logistic distribution (MTBLD). The Fisher information derived in this paper about the parameters contained in the concomitants of order statistics arising from MTBLD justies the possible advantages of applying extreme ranked set sampling. We propose estimators for some unknown parameters of MTBLD based on observations available on both of these two methods of ranked set sampling and make comparison on the efficiency of one estimator over the other.

Residual Renyi Entropy of k-Record Values

ABSTRACT In this article, the residual Renyi entropy (RRE) of k-record values arising from an absolutely continuous distribution is considered. A representation of RRE of k-records arising from an arbitrary distribution in terms of RRE of k-record values arising from uniform distribution is given. Some properties for RRE of k-records are also discussed.

Estimation and Prediction Based on K-Record Values from Logistic Distribution

Abtsrcat In this paper, we consider k-record values arising from logistic distribution. We derive some recurrence relations for the single and product moments of the upper k-record values from logistic distribution. After computing the means, variances and covariances of the upper k-record values, we determine the best linear unbiased estimators for the location and scale parameters of logistic distribution. The best linear unbiased predictor of future k-record values is also determined. Finally, a real data is given to illustrate the inference procedures developed in this paper.