An empirical bayes estimate of multinomial probabilities

This paper deals with the prblem of estimating simultaneously the parameters (Cell probabilities) of m ≤ 2 independent multinomial distributions, with respect to a quadratic loss functions. An empirical Bayes estimator is proposed which is shown to have smaller risk than the maximum likelihood estimator for sufficiently large values of mq, where q is a measure of the average diversity of the given multinomial populations. Some numerical results are given on the performance of the proposed estimator.     

Bayesian Analysis of Different Hybrid and Progressive Life Tests

Type-I and Type-II censoring schemes are the widely used censoring schemes available for life testing experiments. A mixture of Type-I and Type-II censoring schemes is known as a hybrid censoring scheme. Different hybrid censoring schemes have been introduced in recent years. In the last few years, a progressive censoring scheme has also received considerable attention. In this article, we mainly consider the Bayesian inference of the unknown parameters of two-parameter exponential distribution under different hybrid and progressive censoring schemes. It is observed that in general the Bayes estimate and the associated credible interval of any function of the unknown parameters, cannot be obtained in explicit form. We propose to use the Monte Carlo sampling procedure to compute the Bayes estimate and also to construct the associated credible interval. Monte Carlo Simulation experiments have been performed to see the effectiveness of the proposed method in case of Type-I hybrid censored samples. The performances are quite satisfactory. One data analysis has been performed for illustrative purposes.     

An L-statistic approach to a test of exponentiality against IFR alternatives

In this note we consider the problem of testing exponentiality against IFR alternatives. A measure of deviation from exponentiality is developed and a test statistic constructed on the basis of this measure. It is shown that the test statistic is an L-statistic. The asymptotic as well as the exact distribution of the test statistic is obtained and the test is shown to be consistent.     

Setting confidence intervals by ratio estimator

For the survey population total of a variable y when values of an auxiliary variable x are available a popular procedure is to employ the ratio estimator on drawing a simple random sample without replacement (SRSWOR) especially when the size of the sample is large. To set up a confidence interval for the total, various variance estimators are available to pair with the ratio estimator. We add a few more variance estimators studded with asymptotic design-cum-model properties. The ratio estimator is traditionally known to be appropriate when the regression of y on x is linear through the origin and the conditional variance of y given x is proportional to x. But through a numerical exercise by simulation we find the confidence intervals to fare better if the regression line deviates from the origin or if the conditional variance is disproportionate with x. Also, comparing the confidence intervals using alternative variance estimators we find our newly proposed variance estimators to yield favourably competitive results.     

Centrality Asymmetry and Partner Complementarity as Influences on Alliance Dissolution

Research on interfirm alliances indicates that partner firms’ asymmetry in network centrality increases the likelihood of alliance dissolution because it gives rise to a power imbalance and opportunism in the partnership. We contend that this view of centrality asymmetry does not consider the binding force that network resource complementarity can provide in an alliance, which motivates partners to ally for the long term. We propose that centrality asymmetry can have both divisive and cohesive forces in an alliance, which – when considered together – lead to a prediction that centrality asymmetry has a U‐shaped relationship with alliance dissolution. Moderate levels of asymmetry lead to lower rates of dissolution than high and low levels of asymmetry. The degree of cooperation between partners and the degree of external competition reduce the effects of centrality asymmetry on alliance dissolution because they mitigate power imbalances while encouraging partners to strengthen the alliance to withstand competitive challenges.     

Non-mixture cure correlated frailty models in Bayesian approach

In this article, we develop a Bayesian approach for the estimation of two cure correlated frailty models that have been extended to the cure frailty models introduced by Yin [34]. We used the two different type of frailty with bivariate log-normal distribution instead of gamma distribution. A likelihood function was constructed based on a piecewise exponential distribution function. The model parameters were estimated by the Markov chain Monte Carlo method. The comparison of models is based on the Cox correlated frailty model with log-normal distribution. A real data set of bilateral corneal graft rejection was used to compare these models. The results of this data, based on deviance information criteria, showed the advantage of the proposed models.     

Likelihood inference for lognormal data with left truncation and right censoring with an illustration

The lognormal distribution is quite commonly used as a lifetime distribution. Data arising from life-testing and reliability studies are often left truncated and right censored. Here, the EM algorithm is used to estimate the parameters of the lognormal model based on left truncated and right censored data. The maximization step of the algorithm is carried out by two alternative methods, with one involving approximation using Taylor series expansion (leading to approximate maximum likelihood estimate) and the other based on the EM gradient algorithm (Lange, 1995). These two methods are compared based on Monte Carlo simulations. The Fisher scoring method for obtaining the maximum likelihood estimates shows a problem of convergence under this setup, except when the truncation percentage is small. The asymptotic variance-covariance matrix of the MLEs is derived by using the missing information principle (Louis, 1982), and then the asymptotic confidence intervals for scale and shape parameters are obtained and compared with corresponding bootstrap confidence intervals. Finally, some numerical examples are given to illustrate all the methods of inference developed here.     

Identity,Belonging and Intentions to Leave of First and 1.5 Generation FSU Immigrants in Israel

Recent studies examining immigrant intentions to leave the host country have focused on return migration to the origin country (De Haas and Fokkema in Demogr Res 25:755–782, 2011; De Haas et al. in J Int Migr Integr 16(2):415–429, 2015). The current study examines immigrant intentions to leave the host country, but not necessarily to return to the homeland. The predictive model, which focuses on immigrant subjective identity, was tested through a survey of 338 first and 1.5 generation Former Soviet Union (FSU) immigrants in Israel, who applied to a prominent NGO to obtain proof of their Jewishness. These individuals are from the largest recent immigrant group in Israel, and are highly represented among the young adult (aged 25–40) immigrants leaving Israel. The findings indicate that contrary to our expectations, Israeli local identity did not have a significant effect on the immigrants’ intentions to leave Israel. By contrast, Jewish identity and level of religiosity did play a significant role in attenuating the immigrant’s tendency to leave. This paper highlights the complex relationship between ethnic identities and religiosity among immigrants in general, and in Israel in particular.     

Simultaneous estimation of number of signals and signal parameters of superimposed sinusoidal model: A robust sequential bivariate M-periodogram approach

Accurate estimation of the parameters of superimposed sinusoidal signals is an important problem in digital signal processing and time series analysis. In this article, we propose a simultaneous estimation procedure for estimation of the number of signals and signal parameters. The proposed sequential method is based on a robust bivariate M-periodogram and uses the orthogonal structure of the superimposed sinusoidal model for sequential estimation. Extensive simulations and data analysis show that the proposed method has a high degree of frequency resolution capability and can provide robust and efficient estimates of the number of signals and signal parameters.     

Immigrants’ Sense of Belonging to the Host Country: The Role of Life Satisfaction,Language Proficiency,and Religious Motives

This paper focuses on a significant concept in migration studies: immigrants’ sense of belonging to the host society. Drawing upon the literature of immigration and subjective well-being, we proposed a model in which life satisfaction is a major predictor of immigrants’ sense of belonging, and is explained by background variables including religious affiliation, religious motivation, native language proficiency and ethnic segregation. The study was based on a survey of two groups of highly skilled migrants in Israel; immigrants from France and the Former Soviet Union who moved to Israel in the last two decades. The findings suggest that, as expected, life satisfaction had a significant influence on immigrants’ sense of belonging and served as a mediator variable in the model. Whereas ethnic segregation was not found to be a significant parameter in the model, religious motivation and Hebrew language proficiency were found to be prominent. In light of the literature, we also discuss the importance of religious motivations to immigrants’ subjective well-being, identity, and sense of belonging.