A general algorithm fob,univariate and multivariate goodness of pit tests based on graphical representation

This paper presents a general algorithm tor assessing the distributional assumptions. Empirical distributions of the corresponding test statistics are obtained and examples are given to illustrate various applications of the proposed test. By using the squared radii and angles, it is shown that the problem of assessing multivariate normality can be reduced to that of testing for a univariate distribution. A limited comparison is made to investigate the power of the proposed test. This work was supported in part by the National Science Foundation under Grant NO.G88135. Support from the Computer Applications ami Software Engineering (CASE) Center of Syracuse University is also gratefully acknowledged     

Pricing Policy in a Supply Chain: Negotiation or Posted Pricing

This article examines the choice of pricing policy (posted pricing or negotiation) toward end customers in a supply chain. Many retailers actively decide whether or not to encourage negotiation on the shop floor. Of course, the retailer's pricing policy influences not only the retailer's profit, but also the profits of the manufacturers who sell through the retailer. However, little is known about the forces that shape the pricing policy when two self‐interested parties interact in a supply chain. We consider two alternative models depending on who has the power to decide the pricing policy: the manufacturer or the retailer. We find that an increase in the wholesale price weakens the retailer's ability to price discriminate through negotiation. Therefore, the retailer prefers negotiation at lower wholesale prices and posted pricing at higher wholesale prices. We also find that whenever the retailer prefers negotiation, the manufacturer does too. Therefore, the retailer's discretion over the pricing policy causes friction only when the retailer wants to use posted pricing, while the manufacturer wishes the retailer to use negotiation. We show that such friction arises only when product availability or the cost of negotiation is moderate. In this case, we show that the manufacturer may offer a substantial discount to persuade the retailer to negotiate. Surprisingly, in this region of friction, a decrease in the supply chain's capacity or an increase in negotiation costs (both of which are typically considered as worsening the retailer's business environment) translates into higher profit for the retailer.     

The key role of positive parenting and children’s temperament in post‐institutionalized children’s socio‐emotional adjustment after adoption placement. A RCT study

Parenting interventions represent a means for experimentally inquiring socio‐emotional change of post‐institutionalized children after adoption. We used this approach in a three time point RCT study involving 83 post‐institutionalized children (M_age = 33.5 months, SD = 17.1) and their adoptive mothers (M_age = 42.6, SD = 3.9), attending either the Video‐Feedback Intervention to promote Positive Parenting in adoption and foster care or a dummy intervention. Controlling for gender and age at adoption, children showed a significant change in their socio‐emotional adjustment in the specific variables inquired—that is, emotional availability‐EA, and behavioral problems—after intervention attendance. Mediation and moderated mediation models showed that maternal EA was a main factor affecting children’s EA and externalizing behavioral problems, with a key moderating role played by children’s temperament; children with high scores on temperamental negative affect benefitted most from their mothers’ increase in EA.     

Gay Male Academics in UK Business and Management Schools: Negotiating Heteronormativities in Everyday Work Life

This paper contributes to a neglected topic area about lesbian, gay, bisexual and trans people's employment experiences in UK business and management schools. Drawing on queer theory to problematize essentialist notions of sexuality, we explore how gay male academics negotiate and challenge discourses of heteronormativity within different work contexts. Using in‐depth interview data, the paper shows that gay male academics are continually constrained by heteronormativity in constructing viable subject positions as ‘normal’, often having to reproduce heteronormative values that squeeze opportunities for generating non‐heteronormative ‘queer’ sexualities, identities and selves. Constructing a presence as an openly gay academic can invoke another binary through which identities are (re)constructed: as either ‘gay’ (a cleaned up version of gay male sexuality that sustains a heteronormative moral order) or ‘queer’ (cast as radical, disruptive and sexually promiscuous). Data also reveal how gay men challenge organizational heteronormativities through teaching and research activities, producing reverse discourses and creating alternative knowledge/power regimes, despite institutional barriers and risks of perpetuating heteronormative binaries and constructs. Study findings call for pedagogical and research practices that ‘queer’ (rupture, destabilize, disrupt) management knowledge and the heterosexual/homosexual binary, enabling non‐heteronormative voices, perspectives, identities and ways of relating to emerge in queer(er) business and management schools.     

Rank-based ridge estimation in multiple linear regression

Multicollinearity and model misspecification are frequently encountered problems in practice that produce undesirable effects on classical ordinary least squares (OLS) regression estimator. The ridge regression estimator is an important tool to reduce the effects of multicollinearity, but it is still sensitive to a model misspecification of error distribution. Although rank-based statistical inference has desirable robustness properties compared to the OLS procedures, it can be unstable in the presence of multicollinearity. This paper introduces a rank regression estimator for regression parameters and develops tests for general linear hypotheses in a multiple linear regression model. The proposed estimator and the tests have desirable robustness features against the multicollinearity and model misspecification of error distribution. Asymptotic behaviours of the proposed estimator and the test statistics are investigated. Real and simulated data sets are used to demonstrate the feasibility and the performance of the estimator and the tests.     

Parametric estimation of location and scale parameters in ranked set sampling

This paper develops parametric inference for the parameters of location-scale family of distributions based on a ranked set sample. Likelihood function incorporates within-set ranking errors into the model through a missing data mechanism. The maximum likelihood estimators of the location-scale and missing data model parameters are constructed and an EM-algorithm is provided. It is shown that the proposed estimator is robust against imperfect ranking error and provides higher efficiency over its competitors.     

Ratio estimators based on a ranked set sample in a finite population setting

This paper considers the ratio estimator in a finite population setting in a ranked set sampling (RSS) design, where the sample is constructed either with or without replacement policies. It is shown that the proposed ratio estimator is slightly biased, but the amount of bias is smaller than the amount of bias of a simple random sample (SRS) ratio estimator. For the proposed ratio estimator, the paper provides explicit expressions for its mean square error and precision relative to the other competing estimators. It is shown that the new estimator has a substantial amount of improvement in efficiency with respect to SRS estimator. The proposed estimator is applied to two different finite population settings to estimate population mean.     

Social Stratification and Consumption Patterns in Turkey

In this article, by analyzing consumption practices of Turkish households, I attempted to identify whether there are distinctions between different social classes in Turkey. Stated another way, I assessed and explored the impact of socio-economic forces on consumption patterns, taste and lifestyle. In doing so, I tested emprically, two theoretical approaches, Bourdieu’s “reproduction theory” and Giddens “class Structuration thesis”. A total of eight dependent variables are analyzed in terms of the linkages between those selected consumption items and social structure. In general, the emprical findings indicated that the intersection and reinforcement of social class variables, such as income, education, occupation, sector, and neighborhood differentiation, determined consumption patterns and lifestyle differences in Turkey.     

Quantile inference based on partially rank-ordered set samples

This paper develops statistical inference for population quantiles based on a partially rank-ordered set (PROS) sample design. A PROS sample design is similar to a ranked set sample with some clear differences. This design first creates partially rank-ordered subsets by allowing ties whenever the units in a set cannot be ranked with high confidence. It then selects a unit for full measurement at random from one of these partially rank-ordered subsets. The paper develops a point estimator, confidence interval and hypothesis testing procedure for the population quantile of order p. Exact, as well as asymptotic, distribution of the test statistic is derived. It is shown that the null distribution of the test statistic is distribution-free, and statistical inference is reasonably robust against possible ranking errors in ranking process.     

Mixture Model Analysis of Partially Rank‐Ordered Set Samples: Age Groups of Fish from Length‐Frequency Data

We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank‐ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a simple random sample of fish and creates partially rank‐ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional expectation–maximization algorithm is not directly applicable for these observations. We propose a suitable expectation–maximization algorithm to estimate the parameters of the FMMs based on PROS samples. We also study the problem of classification of the PROS sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS samples perform substantially better than their simple random sample counterparts even with small samples. The results are used to classify a fish population using the length‐frequency data.