An iterative Markov rating method

We introduce a simple and natural iterative version of the well-known and widely studied Markov rating method. We show that this iterative Markov method converges to the usual global Markov rating, and shares a close relationship with the well-known Elo rating. Together with recent results on the relationship between the global Markov method and the maximum likelihood estimate of the rating vector in the Bradley–Terry (BT) model, we connect and explore the global and iterative Markov, Elo, and Bradley–Terry ratings on real and simulated data.

Vertex Exchange Method for non-parametric estimation of mixing distributions in logistic mixed models

The conventional normality assumption for the random effects distribution in logistic mixed models can be too restrictive in some applications. In our data example of a longitudinal study modelling employment participation of Australian women, the random effects exhibit non-normality due to a potential mover–stayer scenario. In such a scenario, the women observed to remain in the same initial response state over the study period may consist of two subgroups: latent stayers—those with extremely small probability of transitioning response states—and latent movers, those with a probability of transitioning response states. The similarities between estimating the random effects using non-parametric approaches and mover–stayer models have previously been highlighted. We explore non-parametric approaches to model univariate and bivariate random effects in a potential mover–stayer scenario. As there are limited approaches available to fit the non-parametric maximum likelihood estimate for bivariate random effects in logistic mixed models, we implement the Vertex Exchange Method (VEM) to estimate the random effects in logistic mixed models. The approximation of the non-parametric maximum likelihood estimate derived by the VEM algorithm induces more flexibility of the random effects, identifying regions corresponding to potential latent stayers in the non-employment category in our data example.

ON GENERALISATION OF GOMPERTZ-MAKEHAM DISTRIBUTION

In this paper an exponentiated generalised Gompertz-Makeham distribution. An exponentiated generalised family was introduced by Codeiro, et. al., which allows greater flexibility in the analysis of data. Some Mathematical and Statistical properties including cumulative distribution function, hazard function and survival function of the distribution are derived. The estimation of model parameters are derived via the maximum likelihood estimate method.

Technical efficiency of potato production in mid western terai region of Nepal

A study was done in 2018 to estimate the technical efficiency of potato production in mid western terai region of Nepal.30 households each from Dang, Banke and Bardiya districts were interviewed. Maximum likelihood estimate of the parameter showed the mean technical efficiency of 0.79 which indicated a high scope of increasing the production with the improvement of production technology. The coefficient for the parameter seed, Urea, DAP, MOP and labour were positive contributing for the production of potato. The parameter Compost, pesticides, herbicides and hour of tractor use were negative. The use of these input could be improved for increasing the production of potato. The farm specific variables Education, contact with the extension agent and farm size showed negative coefficient which causes less inefficiency of the farmers in production of potato while the coefficient for Age was estimated to be positive.