Optimal investment and life insurance strategies in a mixed jump-diffusion framework

Abstract

This article investigates an optimal investment and life insurance strategies in a mixed jump-diffusion framework. The individual life insurance policyholder who has CRRA preferences. The market consists of riskless asset, a zero-coupon bond, a stock and life insurance. The instantaneous interest rate is modeled as the O-U model, while a zero-coupon bond with credit risk follows a BSDE and a risky asset be driven by MJD-fBm model. The problem is solved by the mixed jump diffusion fractional HJB SDE which satisfied the admissible strategy, then the closed form solution and optimal strategies are derived and the simulation of the various parameters are also given.     

Variable selection in elliptical linear mixed model

Variable selection in elliptical Linear Mixed Models (LMMs) with a shrinkage penalty function (SPF) is the main scope of this study. SPFs are applied for parameter estimation and variable selection simultaneously. The smoothly clipped absolute deviation penalty (SCAD) is one of the SPFs and it is adapted into the elliptical LMM in this study. The proposed idea is highly applicable to a variety of models which are set up with different distributions such as normal, student-t, Pearson VII, power exponential and so on. Simulation studies and real data example with one of the elliptical distributions show that if the variable selection is also a concern, it is worthwhile to carry on the variable selection and the parameter estimation simultaneously in the elliptical LMM.     

Medicare and Medicaid Reimbursement Rates for Nursing Homes Motivate Select Culture Change Practices But Not Comprehensive Culture Change

Components of nursing home (NH) culture change include resident-centeredness, empowerment, and home likeness, but practices reflective of these components may be found in both traditional and “culture change” NHs. We use mixed methods to examine the presence of culture change practices in the context of an NH’s payer sources. Qualitative data show how higher pay from Medicare versus Medicaid influences implementation of select culture change practices, and quantitative data show NHs with higher proportions of Medicare residents have significantly higher (measured) environmental culture change implementation. Findings indicate that heightened coordination of Medicare and Medicaid could influence NH implementation of reform practices.     

The Attributes of Effective NGOs and the Leadership Values Associated with a Reputation for Organizational Effectiveness

Building on prior research characterizing organizational effectiveness as a social construction, this article identifies the perceived attributes of effective transnational nongovernmental organizations (NGOs) and the leadership values associated with higher reputations for organizational effectiveness. Results are based on an in‐depth, mixed‐method interview study of 152 NGO leaders located in the United States and representing all major sectors of organizational activity. Among the twenty‐nine attributes that leaders identified in peer organizations that they regarded as particularly effective, leaders stressed instantiation of sound principles or strategy, a grassroots approach, large organizational size and resources, being collaborative, singleness of focus, campaigning abilities, funding and fundraising prowess, global scope, and quality people. Furthermore, statistical analysis reveals that NGOs with leaders who value similarities with peer organizations, grassroots approaches, diversity of strategies, dedication, professionalism, and distributed organizational structures have significantly higher reputations for effectiveness.     

A Bayesian estimate of the concordance correlation coefficient with skewed data

Concordance correlation coefficient (CCC) is one of the most popular scaled indices used to evaluate agreement. Most commonly, it is used under the assumption that data is normally distributed. This assumption, however, does not apply to skewed data sets. While methods for the estimation of the CCC of skewed data sets have been introduced and studied, the Bayesian approach and its comparison with the previous methods has been lacking. In this study, we propose a Bayesian method for the estimation of the CCC of skewed data sets and compare it with the best method previously investigated. The proposed method has certain advantages. It tends to outperform the best method studied before when the variation of the data is mainly from the random subject effect instead of error. Furthermore, it allows for greater flexibility in application by enabling incorporation of missing data, confounding covariates, and replications, which was not considered previously. The superiority of this new approach is demonstrated using simulation as well as real‐life biomarker data sets used in an electroencephalography clinical study. The implementation of the Bayesian method is accessible through the Comprehensive R Archive Network. Copyright © 2015 John Wiley & Sons, Ltd.     

‘Keeping the story alive’: is ethnic and racial dilution inevitable for multiracial people and their children?

This paper explores how multiracial parents with White partners articulate narratives of ethnic and racial ‘dilution’ and cultural loss in relation to the socialization of their children. In our broader study of how multiracial parents raise their children, we found that parents commonly spoke of concerns around dilution and generational change in relation to four key themes: the loss of cultural knowledge and diminishing practices that connected parents and their children to a minority ancestry; the embodiment of White‐appearing children and the implications of this for family relationships; the use of biological or genetic discourses in relation to reduced blood quantum; and concerns amongst Black/White participants about whitening and the loss of racial consciousness. Parental understandings of dilution varied greatly; some expressed sadness at ‘inevitable’ loss; others were more philosophical about generational change; and others still proactively countered loss through strategies to connect their children to their minority heritages. We show that despite growing awareness of the social constructedness of race and an emergent cosmopolitanism among these parents, discourses of genetics, cultural lineage, and the ‘naturalness’ of race continue to hold sway amongst many multiracial parents.     

Small Area Estimation Using Estimated Population Level Auxiliary Data

Unit level linear mixed models are often used in small area estimation (SAE), and the empirical best linear unbiased prediction (EBLUP) is widely used for the estimation of small area means under such models. However, EBLUP requires population level auxiliary data, atleast area specific aggregated values. Sometimes population level auxiliary data is either not available or not consistent with the survey data. We describe a SAE method that uses estimated population auxiliary information. Empirical results show that proposed method for SAE produces an efficient set of small area estimates.     

Estimation in a linear regression model with stochastic linear restrictions: a new two-parameter-weighted mixed estimator

The present paper considers the weighted mixed regression estimation of the coefficient vector in a linear regression model with stochastic linear restrictions binding the regression coefficients. We introduce a new two-parameter-weighted mixed estimator (TPWME) by unifying the weighted mixed estimator of Schaffrin and Toutenburg [1] and the two-parameter estimator (TPE) of Özkale and Kaç?ranlar [2]. This new estimator is a general estimator which includes the weighted mixed estimator, the TPE and the restricted two-parameter estimator (RTPE) proposed by Özkale and Kaç?ranlar [2] as special cases. Furthermore, we compare the TPWME with the weighted mixed estimator and the TPE with respect to the matrix mean square error criterion. A numerical example and a Monte Carlo simulation experiment are presented by using different estimators of the biasing parameters to illustrate some of the theoretical results.     

Comparative GMM and GQL logistic regression models on hierarchical data

We often rely on the likelihood to obtain estimates of regression parameters but it is not readily available for generalized linear mixed models (GLMMs). Inferences for the regression coefficients and the covariance parameters are key in these models. We presented alternative approaches for analyzing binary data from a hierarchical structure that do not rely on any distributional assumptions: a generalized quasi-likelihood (GQL) approach and a generalized method of moments (GMM) approach. These are alternative approaches to the typical maximum-likelihood approximation approach in Statistical Analysis System (SAS) such as Laplace approximation (LAP). We examined and compared the performance of GQL and GMM approaches with multiple random effects to the LAP approach as used in PROC GLIMMIX, SAS. The GQL approach tends to produce unbiased estimates, whereas the LAP approach can lead to highly biased estimates for certain scenarios. The GQL approach produces more accurate estimates on both the regression coefficients and the covariance parameters with smaller standard errors as compared to the GMM approach. We found that both GQL and GMM approaches are less likely to result in non-convergence as opposed to the LAP approach. A simulation study was conducted and a numerical example was presented for illustrative purposes.     

Feasible algorithm for linear mixed model for massive data

This article studies computation problem in the context of estimating parameters of linear mixed model for massive data. Our algorithms combine the factored spectrally transformed linear mixed model method with a sequential singular value decomposition calculation algorithm. This combination solves the operation limitation of the method and also makes this algorithm feasible to big dataset, especially when the data has a tall and thin design matrix. Our simulation studies show that our algorithms make the calculation of linear mixed model feasible for massive data on ordinary desktop and have same estimating accuracy with the method based on the whole data.