Inference Based on k-Record Values from Generalized Exponential Distribution

In this paper, the lower k-record values arising from a two parameter generalized exponential distribution is considered. The maximum likelihood estimators for the shape parameter and scale parameter are obtained. The Bayes estimates of the parameters are also developed by using Markov chain Monte Carlo method under symmetric and asymmetric loss functions. Finally, a simulation study is performed to find the performance of different estimators developed in this paper

Patterns of mandibular invasion in oral squamous cell carcinoma of the mandibular region

BACKGROUND: Mandibular resections are routinely carried out for achieving a R0 resection for oral cancers. However, the need of mandibular resection to achieve this has always been questioned. The present study was carried out to define the pattern of mandibular involvement in carcinoma of the mandibular region. PATIENTS AND METHODS: A total of 25 consecutive patients who had undergone mandibular resection and were found to have mandibular invasion were studied in a prospective open fashion. After decalcification the specimens were serially sectioned at 1 cm interval to identify invasion of mandibular bone. Type of invasion, route of spread and host cell reactions were also recorded. RESULTS: The mandibular involvement was infiltrative in 14(56%) and erosive in 11(44%). It was cortical in 5(20%), marrow involvement was seen in 15(60%) while 5(20%) had spread through the inferior alveolar canal. Of the 25, 24(96%) lesions were located with in 1 cm of the mandible. CONCLUSION: The possibility of mandibular involvement is higher in patients where tumours are located with in 1 cm of the mandible. Involvement of mandible through the canal of inferior alveolar nerve in the present study was relatively high (20%). Therefore it is recommended that before a decision is taken to preserve the mandible it should be thoroughly screened for possible involvement

Inference on P(Y < X) Based on Ranked Set Sample for Generalized Pareto Distribution

In this paper, the problem of estimation of R = P(Y &lt; X) based on ranked set sampling, when (X,Y) follows generalised Pareto distribution (GPD) is considered. The maximum likelihood (ML) estimators and Bayes estimators of R are obtained. A Monte Carlo simulation is also performed to study the behaviour of different estimators

Statistical Inference for Gompertz Distribution based on Progressive Type-II Censored Data with Binomial Removals

In this paper, the problem of estimation of parameters for a two-parameterGompertz distribution is considered based on a progressively type-II censored sample with binomial removals. Together with the unknown parameters, the removal probability is also estimated. The maximum likelihood estimators of the parameters and the asymptotic variance-covariance matrix of the estimates are obtained. Bayes estimators are also obtained using different loss functions such as squared error, LINEX and general entropy. A simulation study is performed for comparison between various estimators developed in this paper. A real data set is also used for illustration

Concomitants of Record Values Arising from Morgenstern Type Bivariate Logistic Distribution and Some of their Applications in Parameter Estimation

Concomitants of record values, Morgenstern family of distributions, Morgenstern type bivariate logistic distribution, Estimation using concomitants of record values,

Estimation and prediction based on k-record values from normal distribution

In this paper, we introduce the k-record values arising from normal distribution. After computing the means, variances and covariances of the k-record values, we determine the best linear unbiased estimators for the location and scale parameters of normal distribution based on k-record values. 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

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

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.Ranked set sampling, bivariate Pareto distribution, best linear unbiased estimator,