Estimation and prediction for an inverted exponentiated Rayleigh distribution under hybrid censoring

In this paper we consider estimation of unknown parameters of an inverted exponentiated Rayleigh distribution when it is known that data are hybrid Type I censored. The maximum likelihood and Bayes estimates are derived. In sequel interval estimates are also constructed. We further consider one- and two-sample prediction of future observations and also obtain prediction intervals. The performance of proposed methods of estimation and prediction is studied using simulations and an illustrative example is discussed in support of the suggested methods.     

A Simple Probabilistic Proof for the Alternating Convolution of the Central Binomial Coefficients

This note presents a simple probabilistic proof of the identity for the alternating convolution of the central binomial coefficients. The proof of the identity involves the computation of moments of order n for the product of standard normal random variables.     

Exponentiated Teissier distribution with increasing,decreasing and bathtub hazard functions

This article introduces a two-parameter exponentiated Teissier distribution. It is the main advantage of the distribution to have increasing, decreasing and bathtub shapes for its hazard rate function. The expressions of the ordinary moments, identifiability, quantiles, moments of order statistics, mean residual life function and entropy measure are derived. The skewness and kurtosis of the distribution are explored using the quantiles. In order to study two independent random variables, stress–strength reliability and stochastic orderings are discussed. Estimators based on likelihood, least squares, weighted least squares and product spacings are constructed for estimating the unknown parameters of the distribution. An algorithm is presented for random sample generation from the distribution. Simulation experiments are conducted to compare the performances of the considered estimators of the parameters and percentiles. Three sets of real data are fitted by using the proposed distribution over the competing distributions.     

Forest structure and anthropogenic disturbances regulate plant invasion in urban forests

Urban forest ecosystems, the structure, and functions therein are subjected to anthropogenic disturbances. Native and sensitive species from those forests might be lost due to such disturbances. At the same time, supplemented anthropogenic resources might create opportunities for exotic and invasive species. Although invasive species are considered one of the major threats to the urban biodiversity and ecosystems, the research on invasion dynamics in the Himalayas has primarily focused on the impacts of invasion on forest structure and productivity. This study aims to understand the influence of forest structure and anthropogenic factors in invasion success that are poorly covered in the existing literature. We selected 11 urban forest patches for the study considering the presence-absence of selected invasive species and structural attributes. We used Principal Component Analysis (PCA) to reduce co-linearity in the covariates and generalized linear mixed effects model (GLMM) to identify the factors affecting invasion success. We found that forest structural attributes, namely, tree diameter, height and canopy cover, and anthropogenic disturbances regulate invasion success in urban forests. This implies that maintaining urban forest structural attributes, especially the stands with large-sized trees, is essential to control invasion in the context of urbanization.

     

Optimal Enhancement and Lifetime of Software Systems: A Control Theoretic Analysis

We develop an optimal control model to maximize the net value provided by a software system over its useful life. The model determines the initial number of features in the system, the level of dynamic enhancement effort, and the lifetime of the system. The various factors affecting these optimal choices are systems characteristics (e.g., complexity, age, quality), user learning, and process maturity. We also consider that there is a time lag between the addition of a feature and the realization of its benefit to users. The basic model is extended to consider the decision of replacing the existing system by a new one.     

A Passage to India: Altering Tracks through Paternalistic Welfarism for High Performance in India's Public Sector Rail Undertakings

High-performance work practices (HPWPs) enacted within public sector undertakings (PSUs) in emerging economies are a relatively underexplored topic. By employing the theoretical lens of social exchange and the ability, motivation and opportunity paradigm, this paper highlights the mediating effects of knowledge sharing on the relationship between employee perceptions of HPWPs and employee and business unit performance. We provide evidence drawn from both manager- and employee-level voices by applying a qualitative case study design to two large Indian PSUs and taking a reverse/inverse approach in order to delineate the commonly understood conceptualizations of HPWPs. Our findings confirm the presence of a combination of high-commitment, high-involvement and high-performance work systems that increase the ability, motivation and opportunity of employees to share knowledge, and thus help achieve positive employee and financial outcomes. We found that our set of HPWPs exhibit a strong paternalistic welfarism ethos. We also discuss the implications of our study for research and practice.     

Effect of urbanization on the dynamics of ecosystem services: An analysis for decision making in Kolkata urban agglomeration

Urban Ecosystems - Urbanization has profound influence on the changes of land use and land cover, which on the other hand exert significant impact on ecosystem services and their values, especially...     

Projecting populations for major Pacific Island countries with and without COVID-19: pro-active insights for population policy

Journal of Population Research - The paper projects aggregate populations of six Pacific Island countries in both pre- and post-COVID19 scenarios using a Cohort Component Method for the period...     

Bayesian neural tree models for nonparametric regression

Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.