The debt trap: A two-compartment train wreck… and how to avoid it

We explore sustainable paths out of a debt trap with a highly stylized two-sector differential equations model for the stocks of money in Government and Society. The model fits the data for the U.S. between 1981 and 2012 with a coefficient of correlation of 0.996. The solutions provide detailed “escape conditions” from the debt trap. A primary surplus is required. Then a government can escape its debt trap either through sustained annual monetary outflows from society to the government (taxation) but with a low initial growth rate, or through annual monetary inflows into both sectors (stimulus) with higher initial growth rate. We illustrate the use of our model with simulations which show how five indebted countries can escape their debt trap in 30 (or 70) years.     

Promoting Children's Welfare by Inter‐professional Practice and Learning in Social Work and Primary Care

Social work in the United Kingdom is an activity that benefits from, and often requires, co‐operation between different staff and across different professions. How this is to be achieved has been a central dilemma for practice and policy for many years. Primary care health services often play a key role. New policy and practice developments are designed to promote inter‐professional working with children, where research has shown significant problems in the community‐based health care of looked after children, children leaving care, and children at serious risk. There is, however, little evidence from research that supports current developments in inter‐professional practice; analysis of the particular nature of the inter‐professional problems is lacking, as are therefore the relevant inter‐professional solutions. A framework for better analysis, linked to developments in practice‐based research, one that would yield significant improvements for children's welfare, is presented here.     

Decomposing primary and secondary effects: A new decomposition method

One strand of educational inequality research aims at decomposing the effect of social class origin on educational choices into primary and secondary effects. We formalize this distinction and present a new and simple method that allows empirical assessment of the relative magnitudes of primary and secondary effects. Contrary to other decomposition methods, this new method is unbiased, is more intuitive, and decomposes effects of both discrete and continuous measures of social origin. The method also provides analytically derived statistical tests and is easily calculated with standard statistical software. We give examples using the Danish Longitudinal Survey of Youth.     

Comparing Two Population Means and Variances: A Parametric Robust Way

Abstract

This article introduces a parametric robust way of comparing two population means and two population variances. With large samples the comparison of two means, under model misspecification, is lesser a problem, for, the validity of inference is protected by the central limit theorem. However, the assumption of normality is generally required, so that the inference for the ratio of two variances can be carried out by the familiar F statistic. A parametric robust approach that is insensitive to the distributional assumption will be proposed here. More specifically, it will be demonstrated that the normal likelihood function can be adjusted for asymptotically valid inferences for all underlying distributions with finite fourth moments. The normal likelihood function, on the other hand, is itself robust for the comparison of two means so that no adjustment is needed.     

A Comparison of Survival Function Estimators in the Koziol–Green Model

In this paper, three competing survival function estimators are compared under the assumptions of the so-called Koziol– Green model, which is a simple model of informative random censoring. It is shown that the model specific estimators of Ebrahimi and Abdushukurov, Cheng, and Lin are asymptotically equivalent. Further, exact expressions for the (noncentral) moments of these estimators are given, and their biases are analytically compared with the bias of the familiar Kaplan–Meier estimator. Finally, MSE comparisons of the three estimators are given for some selected rates of censoring.     

Modified minimax estimation of regression coefficients

Working with the linear regression model (1.1) and having the extraneous information (1.2) about regression coefficients the problem exists how to build estimators (1.3) with the risk (1.4) which enable to utilize the known information in order to reduce their risk as compared with the risk (1.6) of the LSE (1.5). Solution of this problem is known for the positive definite matrix T, namely in form for estimators (1.8) and (1.10).First, it is shown that the proposed estimators (2.6),(2.9) and (2.16) based on psedoinversions of the matrix L represent the solution of the problem of the positive semidefinite matrix T=L'L.Further, the problem of interpretability of estimators in the sense of the inequality (3.1) exists; it is shown that all mentioned estimators are at least partially interpretable in the sense of requirements (3.2) or (3.10).     

Estimation with right‐censored observations under a semi‐Markov model

The semi‐Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end‐point of the support of the censoring time is strictly less than the right end‐point of the support of the semi‐Markov kernel, the transition probability of the semi‐Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi‐Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study. The Canadian Journal of Statistics 41: 237–256; 2013 © 2013 Statistical Society of Canada     

A computational investigation of conover's kolmogorov-smirnov test for discrete distributions

The tabled significance values of the Kolmogorov-Smirnov goodness-of-fit statistic determined for continuous underlying distributions are conservative for applications involving discrete underlying distributions. Conover (1972) proposed an efficient method for computing the exact significance level of the Kolmogorov-Smirnov test for discrete distributions; however, he warned against its use for large sample sizes because “the calculations become too difficult.”

In this work we explore the relationship between sample size and the computational effectiveness of Conover's formulas, where “computational effectiveness” is taken to mean the accuracy attained with a fixed precision of machine arithmetic. The nature of the difficulties in calculations is pointed out. It is indicated that, despite these difficulties, Conover's method of computing the Kolmogorov-Smirnov significance level for discrete distributions can still be a useful tool for a wide range of sample sizes.     

Methods for calculating stationary distribution in linear models of time series

The article deals with methods for computing the stationary marginal distribution in linear models of time series. Two approaches are described. First, an algorithm based on approximation of solution of the corresponding integral equation is briefly reviewed. Then, we study the limit behaviour of the partial sums c ₁ η₁+c ₂ η₂+···+c _n η _n where η _i are i.i.d. random variables and c _i real constants. We generalize procedure of Haiman (1998) [Haiman, G., 1998, Upper and lower bounds for the tail of the invariant distribution of some AR(1) processes. Asymptotic Methods in Probability and Statistics, 45, 723–730.] to an arbitrary causal linear process and relax the assumptions of his result significantly. This is achieved by investigating the properties of convolution of densities.