Does greater household wealth make young children perform better? The case of Vietnam

Our study examines factors affecting children's cognitive ability in Vietnam for the period 2006–2016. We find that conditional wealth has a positive association with the cognitive capacity of 15-year-old children, manifested in all three methods of measurement: vocabulary points, math scores and reading comprehension scores in Vietnamese. Notably, the finding implies that improving household wealth after the children's first 1,000 days still plays an important role in the cognitive development of 5–12-year-olds. Also, it suggests that using conditional wealth enables us to capture the impact of economic shocks, thereby having a significant effect on the cognitive ability of children.     

Effectiveness of policy interventions during financial crises in China and Russia: Lessons for the COVID-19 pandemic

We test the hypothesis that policy interventions in crisis periods are less effective when markets are integrated, drawing on China and Russia’s experience during the global financial crisis. We conduct an event study to examine the response of stock market returns and volatility to intervention efforts using DCC-GARCH and Markov Regime Switching Models. We then estimate the extent of integration of China and Russia with the US market and assess its impact on policy interventions’ effectiveness based on a regression framework. We find that interventions were effective in China but failed in Russia, where greater global links were evident. Our findings provide important policy lessons to address the impact of the current COVID-19 pandemic, given the increasing global market linkages.     

Correcting the bias due to dependent censoring of the survival estimator by conditioning

We consider the conditional estimation of the survival function of the time T₂ to a second event as a function of the time T₁ to a first event when there is a censoring mechanism acting on their sum T₁+T₂. The problem has been motivated by a treatment interruption study aimed at improving the quality of life of HIV-infected patients. We base the analysis on the survival function of T₂ given that T₁∈I, where I represents a period of scientific interest (1 trimester, 1 year, 2 years, etc.) and propose a non-parametric estimator for the survival function of T₂ given that T₁∈I, which takes into account both the selection bias and the heterogeneity due to the dependent censoring. The proposed estimator for the survival function uses the risk group of T₂ conditioned on the categories of T₁ and corrects for the dependent censoring using weights defined by the observed values of T₁. The estimator, properly normalized, converges weakly to a zero-mean Gaussian process. We estimate the variance of the limiting process via a bootstrap methodology. Properties of the proposed estimator are illustrated by an extensive simulation study. The motivating data set is analysed by means of this new methodology.     

Inverse Gaussian Distribution for Modeling Conditional Durations in Finance

The durations between market activities such as trades and quotes provide useful information on the underlying assets while analyzing financial time series. In this article, we propose a stochastic conditional duration model based on the inverse Gaussian distribution. The non-monotonic nature of the failure rate of the inverse Gaussian distribution makes it suitable for modeling the durations in financial time series. The parameters of the proposed model are estimated by an efficient importance sampling method. A simulation experiment is conducted to check the performance of the estimators. These estimates are used to compute estimated hazard functions and to compare with the empirical hazard functions. Finally, a real data analysis is provided to illustrate the practical utility of the models.     

A Monte Carlo Method for Estimating Prediction Limit for the Arithmetic Mean of Lognormal Sample

A Monte Carlo (MC) method is suggested for calculating an upper prediction limit for the mean of a future sample of small size N from a lognormal distribution. This is done by obtaining a Monte Carlo estimator of the limit utilizing the future sample generated from the Gibbs sampler. For the Gibbs sampler, a full conditional posterior predictive distribution of each observation in the future sample is derived. The MC method is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries. In an example, practical application of the method is described.     

Empirical Bayes Testing for the Mean Lifetime of Exponential Distributions: Unequal Sample Sizes Case

This paper deals with an empirical Bayes testing problem for the mean lifetimes of exponential distributions with unequal sample sizes. We study a method to construct empirical Bayes tests {δ* _nl + 1,n }^∞ _n = 1 for the sequence of the testing problems. The asymptotic optimality of {δ* _nl + 1,n }^∞ _n = 1 is studied. It is shown that the sequence of empirical Bayes tests {δ* _nl + 1,n }^∞ _n = 1 is asymptotically optimal, and its associated sequence of regrets converges to zero at a rate (ln n)^4M?1/n, where M is an upper bound of sample sizes.     

A unified approach to binomial-type distributions of order k

In this paper, we consider the distribution of the number of "1"-runs of length k in a sequence of {0,1}-valued random variables of length n by using a new (unified) counting scheme called l-overlapping counting. Here, k and n are positive integers with k ≦ and l is an integer less than k. We obtain the prohabi!ity generating function of the distribution of the number of eoverlapping "in-runs of iength k in the sequence, even when the underiying sequence is a dependent sequence such as a highcr order Markov chaic.     

Bayes and Empirical Bayes Two-Stage Procedures for Sampling Inspection

A batch of M items is inspected for defectives. Suppose there are d defective items in the batch. Let d ₀ be a given standard used to evaluate the quality of the population where 0 < d ₀ < M. The problem of testing H ₀: d < d ₀ versus H ₁: d ≥ d ₀ is considered. It is assumed that past observations are available when the current testing problem is considered. Accordingly, the empirical Bayes approach is employed. By using information obtained from the past data, an empirical Bayes two-stage testing procedure is developed. The associated asymptotic optimality is investigated. It is proved that the rate of convergence of the empirical Bayes two-stage testing procedure is of order O (exp(? c? n)), for some constant c? > 0, where n is the number of past observations at hand.     

Maximum and Minimum Prediction Variance for Spherical and Cuboidal Split Plot Designs

The impact of restricted randomization on the information matrix has created challenges for the computation of design optimality criteria. This article focuses on the computation of the maximum and minimum prediction variance for Central Composite (CCD) and Box–Behnken (BBD) split plot designs (SPD). The approach is to analytically determine the exact maximum and minimum prediction variance for both spherical and cuboidal second-order SPD. A particular feature of these analytical functions is that they are functions of the design parameters. Finally, the application of these analytical functions is demonstrated for a CCD SPD.