‘Growing Old’ in Shelters and ‘On the Street’: Experiences of Older Homeless People

Homelessness among older people in Canada is both a growing concern, and an emerging field of study. This article reports thematic results of qualitative interviews with 40 people aged 46 to 75, carried out as part of a mixed-methods study of older people who are homeless in Montreal, Quebec, Canada. Our participants included people with histories of homelessness (n = 14) and persons new to homelessness in later life (n = 26). Interviews focused on experiences at the intersections of aging and homelessness including social relationships, the challenges of living on the streets and in shelters in later life, and the future. This article outlines the 5 main themes that capture the experience of homelessness for our participants: age exacerbates worries; exclusion and isolation; managing significant challenges; shifting needs and realities; and resilience, strength, and hope. Together, these findings underscore the need for specific programs geared to the unique needs of older people who are homeless.     

Multinomial Selection in the Presence of Infinite Alternatives

We propose a new procedure for the multinomial selection problem to solve a real problem of any modern Air Force: the elaboration of better air-to-air tactics for Beyond Visual Range air-to-air combat that maximize its aircraft survival probability H(θ, ω), as well as enemy aircraft downing probability G(θ, ω). In this study, using a low-resolution simulator with generic parameters for the aircraft and missiles, we could increase an average success rate of 16.69% and 16.23% for H(θ, ω) and G(θ, ω), respectively, to an average success rate of 76.85% and 79.30%. We can assure with low probability of being wrong that the selected tactic has greater probability of yielding greater success rates in both H(θ, ω) and G(θ, ω) than any simulated tactic.     

A Deprivation Analysis for Andalusia (Spain): An Approach Based on Structural Equations

The study of deprivation, as a social indicator, is basic in the design and development of public policies because it allows decision makers to identify and analyse needy areas in order to improve their citizens’ well-being. The methodological approach proposed for the development of a new deprivation index is based on the Causal Theory whose conceptual model is analysed using Structural Equations. The domains selected for the deprivation index are: education, employment, income, housing, infrastructures and health. A structural equation model based on variance is the exploratory method used to obtain the indices pertaining to the above mentioned areas; the results obtained are seen to be quite reliable. There is a positive connection between the areas of education, employment and income while the relations between infrastructures and health are found to be negative. The results can be projected at a local level and show basic territorial deficiencies. The spatial units studied are the Andalusian (south of Spain) municipalities (770). The spatial projection of the indices obtained for the domains of deprivation highlights the existence of geographical areas which could be a potential target for public action.     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.     

Nonparametric Shrinkage Estimation for Aalen's Additive Hazards Model

Aalen's nonparametric additive model in which the regression coefficients are assumed to be unspecified functions of time is a flexible alternative to Cox's proportional hazards model when the proportionality assumption is in doubt. In this paper, we incorporate a general linear hypothesis into the estimation of the time‐varying regression coefficients. We combine unrestricted least squares estimators and estimators that are restricted by the linear hypothesis and produce James‐Stein‐type shrinkage estimators of the regression coefficients. We develop the asymptotic joint distribution of such restricted and unrestricted estimators and use this to study the relative performance of the proposed estimators via their integrated asymptotic distributional risks. We conduct Monte Carlo simulations to examine the relative performance of the estimators in terms of their integrated mean square errors. We also compare the performance of the proposed estimators with a recently devised LASSO estimator as well as with ridge‐type estimators both via simulations and data on the survival of primary billiary cirhosis patients.     

Production and changeover control policies of a class of failure prone buffered flow-shops

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.     

Graphical and Numerical Methods for Detecting Influential Observations in Complex Bingham Data

We describe methods to detect influential observations in a sample of pre-shapes when the underlying distribution is assumed to be complex Bingham. One of these methods is based on Cook's distance, which is derived from the likelihood of the complex Bingham distribution. Other method is related to the tangent space, which is based on the local influence for the multivariate normal distribution. A method to detect outliers is also explained. The application of the methods is illustrated in both a real dataset and a simulated sample.     

The Multi-dimensional Characteristics of Wellbeing: How Different Aspects of Wellbeing Interact and Do Not Interact with Each Other

Because wellbeing is a multi-varied and dynamic phenomenon, social scientists need to better understand how different aspects of people’s wellbeing are tied together and how these ties differ between individuals at one point in time and within individuals over time. The paper outlines a general model that considers the multidimensional structure of wellbeing. We utilize the potential of latent variable modelling on a unique Swedish database: the Panel Survey of Ageing and the Elderly (PSAE). An inclusive and flexible model that considers the interactions between semi-autonomous domains (material resources, health, psychosocial factors, lifestyle, etc.) is developed. Our empirical study is based on cross-sectional PSAE data and focuses on people 55 years or older (N = 5,374). The analysis takes advantage of recent developments in statistical theory in the field of latent variable modelling to bring about a more dynamic and theory confirming analysis of a multifaceted phenomenon such as wellbeing.     

Characteristics of a central change programme within a governmental bureaucracy: a grounded theory study

Purpose The purpose of the paper is to present a theory of organisational change within the setting of a governmental bureaucracy. Design/methodology/approach Orthodox grounded theory is employed in the setting of a change programme in 12 Audit departments of the Dutch Ministries (public-sector). Findings The examined organisational change has specific characteristics. The Dutch ‘ministerial autonomy’ and the clearly recognizable role types of the key figures involved have a strong impact on the change programme and the realisation of its goals. Research limitations/implications Findings are derived on the basis of a substantive case study of the change programme of the Dutch Departmental Audit function. The research sheds new light on characteristics related to the occupational group of Chartered Accountants and characteristics of a governmental bureaucracy and it helps to understand change programmes within this bureaucracy. Practical implications The grounded theoretical model draws attention to the impact of key figures and circumstances on a change programme within the central government. Originality/value The paper builds on theories of organisational change but applies them in the typical context of the Dutch civil service where ministerial autonomy, on the one hand, and a move in the direction of more centrally organised activities (concern idea), on the other, require a difficult balancing act of all players in the change arena.     

Evaluation of scheduling strategies for a dynamic job shop in a tool-shared,flexible manufacturing environment

A two-phase approach is used to examine the impact of job scheduling rules and tool selection policies for a dynamic job shop system in a tool-shared, flexible manufacturing environment. The first phase develops a generalized simulation model and analyses 'simple' job scheduling rules and tool selection policies under various operating scenarios. The results from this investigation are then used to develop and analyse various bi-criteria rules in the second phase of this study. The results show that the scheduling rules have the most significant impact on system performance, particularly at high shop load levels. Tool selection policies affect some of the performance measures, most notably, proportion of tardy jobs, to a lesser degree. Higher machine utilizations can be obtained at higher tool duplication levels but at the expense of increased tooling costs and lower tool utilization. The results also show that using different processing time distributions may have a significant impact on shop performance.