Seasonality and vertical structure of light-attracted insect communities in a dipterocarp forest in Sarawak

Nocturnal flying insects were collected monthly for 13 months using ultra violet light-traps set at various vertical levels in a weakly-seasonal, tropical lowland dipterocarp forest in Sarawak, Malaysia. Abundance, faunal composition, size distribution and guild structure of these samples were analyzed with respect to temperal and vertical distributions. The nocturnal flying insect community in the canopy level was highly dominated by fig wasps (84%) in individual number, and by scarabaeid beetles (28%) in weight. A principal component analysis on monthly catches detected non-random, seasonal trends of insect abundance. The first two principal trends were an alternation of wetter (September to January) and less wet seasons (February to August) and an alternation between the least wet (January to March) and the other seasons. Many insect groups were less abundant in the least wet season than the other seasons, whilst inverse patterns were found in Scarabaeidae and Tenebrionidae. Significantly positive and negative correlations between monthly catch and rainfall were detected only in ovule-feeders and in phloem-feeders, respectively. Delayed, significant negative correlations between monthly catch and 1–3 month preceding rainfall were more frequently detected in phytophages, phloem-feeders, seed-feeders, wood-borers and scavengers. The peak in abundance along vertical levels were found at the canopy level (35 m) for phloem-, ovule-, seed-, root-, fungal-feeders and nectar collectors, at an upper subcanopy level (25 m) for scavengers and aquatic predators, and at a middle subcanopy level (17 m) for ants. Catches at the emergent level (45 m) did not exceed those at the canopy level.     

A new extended generalized Gompertz distribution with statistical properties and simulations

Abstract

Statistical distributions are very useful in describing and predicting real world phenomena. In many applied areas there is a clear need for the extended forms of the well-known distributions. Generally, the new distributions are more flexible to model real data that present a high degree of skewness and kurtosis. The choice of the best-suited statistical distribution for modeling data is very important.

In this article, we proposed an extended generalized Gompertz (EGGo) family of EGGo. Certain statistical properties of EGGo family including distribution shapes, hazard function, skewness, limit behavior, moments and order statistics are discussed. The flexibility of this family is assessed by its application to real data sets and comparison with other competing distributions. The maximum likelihood equations for estimating the parameters based on real data are given. The performances of the estimators such as maximum likelihood estimators, least squares estimators, weighted least squares estimators, Cramer-von-Mises estimators, Anderson-Darling estimators and right tailed Anderson-Darling estimators are discussed. The likelihood ratio test is derived to illustrate that the EGGo distribution is better than other nested models in fitting data set or not. We use R software for simulation in order to perform applications and test the validity of this model.     

The Labor Market Returns to AACSB Accreditation

We examine whether there is a wage premium from attending an AACSB accredited business college compared to a non-accredited business college. To estimate the returns to AACSB accreditation, we use data from the 1993/1994 Baccalaureate and Beyond Longitudinal Study, which provides a rich set of control variables. The earnings regressions suggest that there is no wage premium associated with graduating from an AACSB accredited business college upon entry into the workforce.     

The Dyadic Effects of Controlling Emotional Abuse on Subjective Emotional Experience in Iranian Older Married Couples

This randomised control trial study examines the impact of an emotional‐focused intervention on emotional abuse and subjective emotional experience in Iranian older married couples. In the study, 57 older couples were randomly assigned into control and experimental groups with the latter taking part in an intervention program over 4 weeks, twice a week, with sessions lasting 90 minutes. In the experimental group, significant improvements were found for reducing emotional abuse behaviours and increasing subjective emotional experience. Dyadic analysis revealed that a partner's ability to control their own emotional abuse significantly effects both his/her subjective emotional experience and that of their spouse.     

Robust estimation of the mean vector for high-dimensional data set using robust clustering

The first step in statistical analysis is the parameter estimation. In multivariate analysis, one of the parameters of interest to be estimated is the mean vector. In multivariate statistical analysis, it is usually assumed that the data come from a multivariate normal distribution. In this situation, the maximum likelihood estimator (MLE), that is, the sample mean vector, is the best estimator. However, when outliers exist in the data, the use of sample mean vector will result in poor estimation. So, other estimators which are robust to the existence of outliers should be used. The most popular robust multivariate estimator for estimating the mean vector is S-estimator with desirable properties. However, computing this estimator requires the use of a robust estimate of mean vector as a starting point. Usually minimum volume ellipsoid (MVE) is used as a starting point in computing S-estimator. For high-dimensional data computing, the MVE takes too much time. In some cases, this time is so large that the existing computers cannot perform the computation. In addition to the computation time, for high-dimensional data set the MVE method is not precise. In this paper, a robust starting point for S-estimator based on robust clustering is proposed which could be used for estimating the mean vector of the high-dimensional data. The performance of the proposed estimator in the presence of outliers is studied and the results indicate that the proposed estimator performs precisely and much better than some of the existing robust estimators for high-dimensional data.     

Will a Risk-Averse Decision Maker Ever Really Prefer an Unfair Gamble? Sometimes,He Will

The comments of Horowitz (1998) on the Prakash, Chang, Hamid, and Smyser (1996) paper are specifically directed towards Lemma 4 of the original paper. This reply proposes that Horowitz fails to recognize that the conditions stated are only necessary but not sufficient, and solutions can be obtained for some specific utility function for a given level of wealth in the case of a neutral gamble and for a range of wealth for unfair gambles. We derive the conditions to show that a risk preference function that satisfies the restrictions of Lemma 4 can exist for a given level of wealth.     

Monitoring two-stage processes with a profile at the second stage

In the multistage processes, quality of a process or a product at each stage is related to the previous stage(s). This property is referred to as a cascade property. Sometimes, quality of a process is characterized by a profile. In this paper, we consider a two-stage process with a normal quality characteristic in the first stage and a simple linear regression profile in the second stage. Then we propose two methods to monitor quality characteristics in both stages. The performance of the proposed two methods is evaluated through a numerical example in terms of average run length criterion.     

A modified DEA cross efficiency method with negative data and its application in supplier selection

Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.