The mean residual of record values at the level of previous records

In the study of reliability of the technical systems, records model plays an important role. Suppose that a technical system is subject to shocks, e.g. peaks of voltages or stresses. The successive large shocks may be viewed as realizations of records from a sequence of identically independent voltages. Assume that the lower limit value of the mth record voltage (stress) is v > 0. Under these conditions, we propose a mean residual of records (MRR’s) for the future records. We study several properties of MRR. We show that the proposed MRR uniquely characterizes the distribution function that generated the sequence of records. It is proved that when the model under study has an increasing hazard rate the corresponding MRR is decreasing. We also compare between two record systems based on their MRR’s when these systems are ordered in terms of their hazard rates.     

Bounds for the mean residual life function of a <Emphasis Type="Italic">k</Emphasis>-out-of-<Emphasis Type="Italic">n</Emphasis> system

In the reliability studies, k-out-of-n systems play an important role. In this paper, we consider sharp bounds for the mean residual life function of a k-out-of-n system consisting of n identical components with independent lifetimes having a common distribution function F, measured in location and scale units of the residual life random variable X _t  = (X−t|X > t). We characterize the probability distributions for which the bounds are attained. We also evaluate the so obtained bounds numerically for various choices of k and n.     

Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain

This article presents the empirical Bayes method for estimation of the transition probabilities of a generalized finite stationary Markov chain whose ith state is a multi-way contingency table. We use a log-linear model to describe the relationship between factors in each state. The prior knowledge about the main effects and interactions will be described by a conjugate prior. Following the Bayesian paradigm, the Bayes and empirical Bayes estimators relative to various loss functions are obtained. These procedures are illustrated by a real example. Finally, asymptotic normality of the empirical Bayes estimators are established.     

A fuzzy goal programming approach to analyse sustainable development goals of India

Energy policy, environmental planning and economic development play a key role in sustainable development. Sustainable development requires suitable and strategic policies satisfying multiple and conflicting objectives. Fuzzy goal programming (FGP) is a well-known approach in multi-criteria decision-making for its practical application. In this article, a FGP approach is proposed to analyse environmental, energy and sustainability goals of India by the year 2030 with reference to the key economic sectors of India. The presented model analyses the improvement opportunities, requirement of efforts and implementation of the sustainable development plans. Numerical illustration is also provided for validation and application of the proposed model.     

Comparative performances of measures of consumer and economic sentiment in forecasting consumption: a multi-country analysis

ABSTRACT

This paper seeks to compare the capabilities of assorted measures of consumer and economic sentiment in predicting the growth of household expenditure. An analysis of quarterly data on five European countries shows that for none of these can the model which incorporates the EU’s headline consumer confidence indicator be deemed to be significantly inferior to any of its seven rivals. However, the rankings of the sentiment variables are seen to be influenced by: the proportion of total spending by households that is devoted to durable goods; and the nature of the behaviour of consumption over the forecast interval.     

Underperformance of Actively Managed Portfolios: Some Behavioral Insights

Abstract

Two glaring anomalies in investment management are apparent: (1) after fees, active portfolio managers do worse than market indices, and (2) clients continue to pay for services they don’t receive. The purpose of this paper is to offer explanations of these anomalies from a behavioral perspective. We explore some of the cognitive biases that perpetuate active management and subsequent underperformance, including herding, disposition, and endowment effects, as well as conservatism and status quo biases, overconfidence, and agency problems. Investors’ continued use of active managers despite persistent disappointing returns is attributed to being victims of framing effect, hot-hand fallacy, lack of knowledge as well as intimidation or insecurity, and status quo bias. We propose some ways that portfolio managers and investors could improve their decision making.     

Vicarious warfare and dynamic efficiency of companies in the aerospace and defence industry

This study explores the economic performance that vicarious warfare can bring for the aerospace and defence industry. First, this study measures the performance of 22 companies using a nonradial dynamic data envelopment analysis model from 2011 to 2017. Second, this study evaluates whether vicarious warfare can influence the performance. The efficiency analysis suggests rooms for improvements meanwhile, the projection analysis suggests that market value should be significantly increased, and the number of employees should be reduced significantly. The regression analysis suggests that the number of times a country takes a side in wars would positively affect the efficiency of companies.     

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.     

Multi- Objective Optimal Design of on- Demand Pressurized Irrigation Networks

In the context of water as an economic good, from the use of water, one can derive a value, which can be affected by the reliability of supply. On-demand irrigation systems provide valuable water to skilled farmers who have the capacity to maximize economic value of water. In this study, simultaneous optimization of on-demand irrigation network layout and pipe sizes is considered taking into account both investment and annual energy costs. The optimization problem is formulated as a problem of searching for the upstream head value, which minimizes the total cost (investment and energy costs) of the system. The investment and annual energy costs are obtained in two separate phases. Max–Min ant system (MMAS) algorithm is used to obtain the minimum cost design considering layout and pipe diameters of the network simultaneously. Clement methodology is used to determine flow rates of pipelines at the peak period of irrigation requirements. The applicability of the proposed method is showed by re-designing a real world example from literature.