Two-Dimensional Warranty Cost Analysis for Second-Hand Products

In spite of the recent steady increase of the volume of the second-hand markets, often customers remain in doubt regarding the quality and durability of the second-hand products. Aiming to reduce and share this uncertainty, dealers offer warranty on their products. Offering warranty for second-hand products is a relatively new marketing strategy employed by dealers of used electronic equipment, furniture, automobiles, etc. Usually, for used products, the dealer's expected warranty cost is a function of product reliability, past age and usage, servicing strategy and conditions and terms of the warranty policy/contract. Sometimes the offered policy is limited by two parameters, typically the product age and usage after the sale. This type of policies is referred to as two-dimensional warranty policies. In this article, we develop statistical models for estimating the dealer's expected warranty cost for second-hand products sold with two-dimensional free repair/replacement warranty.     

Robust Estimation of Multi-response Surfaces Considering Correlation Structure

Response surfaces express the behavior of responses and can be used for both single and multi-response problems. A common approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Although there are many methods available in the literature for multiple response optimizations, there are a few studies in model building especially robust models. Assuming correlated responses, in this paper, a robust coefficient estimation method is proposed for multi response problem based on M-estimators. In order to illustrate the performance of the proposed procedure, a contaminated experimental design using a numerical example available in the literature with some modifications is used. Both the classical multivariate least squares method and the proposed robust multivariate approach are used to estimate regression coefficients of multi-response surfaces based on this example. Moreover, a comparison of the proposed robust multi response surface (RMRS) approach with separate robust estimation of single response show that the proposed approach is more efficient.     

Combined estimating function for random coefficient models with correlated errors

ABSTRACT

Estimating functionshave been shown to be convenient to study inference for non linear time series models. Recently, Thavaneswaran et al. (2012 Thavaneswaran, A., Liang, Y., Frank, J. (2012). Inference for random coefficient volatility models. Stat. Probab. Lett. 82(12):2086–2090.[Crossref], [Web of Science ®] , [Google Scholar]) used combined estimating functions to study inference for random coefficient autoregressive (RCA) models with generalized autoregressive heteroscedasticity errors. While most RCA modeling assumes that the random term and the error are independent, Chandra and Taniguchi (2001 Chandra, S.A., Taniguchi, M. (2001). Estimating functions for nonlinear time series models. Ann. Inst. Stat. Math 53(1):125–141.[Crossref], [Web of Science ®] , [Google Scholar]) studied inference for RCA models with correlated errors using linear estimating functions. In this paper, we derive the quadratic estimating functions for the joint estimation of the conditional mean, variance, and correlation parameters of the RCA models with correlated errors.     

The frequency of sexual abuse and depression in a sample of street children of one of deprived districts of Tehran

The purpose of this study was to determine the frequency of sexual abuse and depression among street children who live in a deprived district of Tehran. The researchers used the K-SADS questionnaire and a clinical interview were used to evaluate depression and sexual abuse in 87 street children in Tehran. Eighteen (20.9%) of the children had been sexually abused. Depressed children were 3.2 times more likely to be sexually abused than non-depressed children. Furthermore, 26 girls (86.7%) and 27 boys (48.2%) suffered from depression. The frequency of depression demonstrated a significant association with the father's or breadwinner's history of imprisonment or unemployment. Interventional programs providing education and support should be implemented for street children.     

Iranian College Students’ Attitudes Toward Premarital Sex,Marriage, and Family Before and After the Islamic Revolution

ABSTRACT. Objectives: The purpose of this study was to examine Iranian college students’ attitudes toward premarital sex and marriage before and after the Islamic Revolution. Methods: Responses to a survey from 526 students at University of Shiraz in 2011 were compared to those from another study of 392 students from the same university who completed the same survey in 1977 (2 years before the Islamic Revolution). Results: No substantial changes were observed to indicate that college students adopted theocratic views on premarital sex and marriage after the Islamic Revolution. Conclusions: The Islamic regime's efforts to cultivate conservative attitudes toward sexuality and marriage have not achieved their goals.     

A Diagnostic Tree for Improving Production Line Performance

Improving performance of production systems is a critical but often unstructured activity. To help managers convert ad hoc or trial & error improvement efforts into efficient and systematic reviews, we develop a diagnostic tree which decomposes a performance improvement objective into successively more concrete sub‐objectives and finally into potential improvement strategies. Based on principles from the Operations Management literature, this tree is structured to enable a non‐specialist to better understand the links between corrective actions and performance. It also provides an important foundation for a principles‐based knowledge management system that couples the decision tree with a search engine for locating relevant documents within an intranet.     

Bayesian bridge quantile regression

Regularization methods for simultaneous variable selection and coefficient estimation have been shown to be effective in quantile regression in improving the prediction accuracy. In this article, we propose the Bayesian bridge for variable selection and coefficient estimation in quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a scale mixture of uniform representation of the Bayesian bridge prior. This is the first work to discuss regularized quantile regression with the bridge penalty. Both simulated and real data examples show that the proposed method often outperforms quantile regression without regularization, lasso quantile regression, and Bayesian lasso quantile regression.     

Phase-I monitoring of general linear profiles in multistage processes

In this article, the general linear profile-monitoring problem in multistage processes is addressed. An approach based on the U statistic is first proposed to remove the effect of the cascade property in multistage processes. Then, the T² chart and a likelihood ratio test (LRT)-based scheme on the adjusted parameters are constructed for Phase-I monitoring of the parameters of general linear profiles in each stage. Using simulation experiments, the performance of the proposed methods is evaluated and compared in terms of the signal probability for both weak and strong autocorrelations, for processes with two and three stages, as well as for two sample sizes. According to the results, the effect of the cascade property is effectively removed and hence each stage can be monitored independently. In addition, the result shows that the LRT approach provides significantly better results than the T² method and outperforms it under different shift and autocorrelation scenarios. Moreover, the proposed methods perform better when larger sample sizes are used in the process. Two illustrative examples, including a real case and a simulated example, are used to show the applicability of the proposed methods.     

Quality of Life of Afghan Immigrants in Tehran city

By 2015, there were more than two million Afghan immigrants, both legal and illegal, living in Iran. Although, over the recent years, there has been a growing interest among social scientists, policymakers and national and international institutes in investigation of the quality of life (QoL) of immigrants, research on the QoL of Afghan immigrants in Iran is still in its infancy. The present article aims to study the QoL of Afghan immigrants in Iran and identify its influential dimensions and factors. This study relies on a perception survey of 347 Afghan households in Tehran city. Based on the 5‐point Likert Scale, the mean of overall life satisfaction of the Afghan immigrants was found as 3.22, which shows satisfactory QoL of Afghan immigrants in Iran. The highest level of satisfaction, with a mean value of 3.96, is related to public transport access followed by satisfaction in social relations with friends, relatives and fellow immigrants living in Iran, and access to cultural centres such as the mosque, library, and cinema. The lowest level of satisfaction, with a mean value of 1.90, was seen at border services like entry and exit from Iran, followed by the Iranian government policy towards Afghan refugees, and saving abilities. In addition, the most important predictors of immigrants' QoL include indicators of health, security, work status, and income, which are closely interrelated with the main reasons for Afghans' immigration to Iran. After immigration to Iran, the QoL status of the immigrants has improved significantly, especially in the fields of security and education; nevertheless, their employment in jobs with low skill and income, along with the decrease in the economic growth of Iran over the recent years, have caused problems for the economic and financial status of the immigrants. The results and findings of this study will be useful for designing and implementing plans and policies necessary to improve QoL of Afghan immigrants in Iran.     

A doubly-inflated Poisson regression for correlated count data

Count data have emerged in many applied research areas. In recent years, there has been a considerable interest in models for count data. In modelling such data, it is common to face a large frequency of zeroes. The data are regarded as zero-inflated when the frequency of observed zeroes is larger than what is expected from a theoretical distribution such as Poisson distribution, as a standard model for analysing count data. Data analysis, using the simple Poisson model, may lead to over-dispersion. Several classes of different mixture models were proposed for handling zero-inflated data. But they do not apply to cases when inflated counts happen at some other points, in addition to zero. In these cases, a doubly-inflated Poisson model has been suggested which only be used for cross-sectional data and cannot consider correlations between observations. However, correlated count data have a large application, especially in the health and medical fields. The present study aims to introduce a Doubly-Inflated Poisson models with random effect for correlated doubly-inflated data. Then, the best performance of the proposed method is shown via different simulation scenarios. Finally, the proposed model is applied to a dental study.KEYWORDS: Count data, doubly-inflated, Poisson regression, zero-inflated, correlated data