Optimal estimation in functional linear regression for sparse noise‐contaminated data

In this article, we propose a novel approach to fit a functional linear regression in which both the response and the predictor are functions. We consider the case where the response and the predictor processes are both sparsely sampled at random time points and are contaminated with random errors. In addition, the random times are allowed to be different for the measurements of the predictor and the response functions. The aforementioned situation often occurs in longitudinal data settings. To estimate the covariance and the cross‐covariance functions, we use a regularization method over a reproducing kernel Hilbert space. The estimate of the cross‐covariance function is used to obtain estimates of the regression coefficient function and of the functional singular components. We derive the convergence rates of the proposed cross‐covariance, the regression coefficient, and the singular component function estimators. Furthermore, we show that, under some regularity conditions, the estimator of the coefficient function has a minimax optimal rate. We conduct a simulation study and demonstrate merits of the proposed method by comparing it to some other existing methods in the literature. We illustrate the method by an example of an application to a real‐world air quality dataset. The Canadian Journal of Statistics 47: 524–559; 2019 © 2019 Statistical Society of Canada     

On the ratio of two independent skewnesses

In many fields, the researchers are interested in making inferences about the ratio of skewnesses in two independent populations. In the present paper, the asymptotic distribution for the ratio of the sample skewnesses in two independent populations is established. Then the asymptotic distribution is used to derive the asymptotic confidence interval and to test the hypothesis for the ratio of population's skewnesses. Finally, the applicability of the proposed method is investigated through Monte Carlo simulations.     

Local Box–Cox transformation on time-varying parametric models for smoothing estimation of conditional CDF with longitudinal data

Nonparametric estimation and inferences of conditional distribution functions with longitudinal data have important applications in biomedical studies, such as epidemiological studies and longitudinal clinical trials. Estimation approaches without any structural assumptions may lead to inadequate and numerically unstable estimators in practice. We propose in this paper a nonparametric approach based on time-varying parametric models for estimating the conditional distribution functions with a longitudinal sample. Our model assumes that the conditional distribution of the outcome variable at each given time point can be approximated by a parametric model after local Box–Cox transformation. Our estimation is based on a two-step smoothing method, in which we first obtain the raw estimators of the conditional distribution functions at a set of disjoint time points, and then compute the final estimators at any time by smoothing the raw estimators. Applications of our two-step estimation method have been demonstrated through a large epidemiological study of childhood growth and blood pressure. Finite sample properties of our procedures are investigated through a simulation study. Application and simulation results show that smoothing estimation from time-variant parametric models outperforms the existing kernel smoothing estimator by producing narrower pointwise bootstrap confidence band and smaller root mean squared error.     

Three-level control charts with variable sample size,sampling interval,and control limits

Recently, three-level control chart has been developed for monitoring situations in which an appropriate quality measure uses three discrete levels to classify a product characteristic. In this paper, the variable parameters control charts for multinomial data are developed with a three-level classification scheme. We compare it with various adaptive charts to show that this modified scheme can improve the performance. In order to evaluate and compare the performance of this scheme, adjusted average time to signal is used as the performance measure. Results indicate that the proposed chart has improved performance and is relatively sensitive to small shifts.     

Evaluating the potential contribution of urban ecosystem service to climate change mitigation

Urban Ecosystems - Promoting urban greenery through tree planting strategies has been considered as a measure to mitigate climate change. While it is essential to understand the temporal dynamics...     

Divorce and the cost of housing: evidence from Iran

Divorce trend in Iran has become a serious social concern that is suspected of being influenced by rising housing costs in an oil-based economy. Iran has the highest growth rate of divorce among Islamic countries in the Middle East and North Africa region. Using data from 30 provinces of Iran from 2002 to 2010, this paper examines the relationship between housing costs (house prices and rents) and divorce rate, controlling for other macroeconomic variables such as unemployment, inflation, and education in addition to regional, cultural, traditional, and conventional attitudes toward divorce. By applying panel fixed-effects and dynamic generalized methods of moments methods, our results suggest that increases in housing costs erode marital stability in Iran. Our main results are also supported when we focus on the shocks in housing costs, using the Vector autoregressive based impulse response and variance decomposition analyses of divorce rates at the national level from 1982 to 2010.     

The Clustering of Psychiatric Disorders in High-Risk Gambling Populations

The aims of this study were to examine the associations between psychiatric disorders and pathological gambling (PG) and the clustering of psychiatric disorders in high risk gambler populations. The sample comprised 140 regular gamblers who were recruited from the general public. A variety of self- report and semi structured questionnaires was administered, including the Mini International Neuropsychiatric Interview, The Personality Diagnostic Questionnaire-4th Edition, NORC DSM-IV Screen for Gambling Problems Self- administered and Problem Gambling Severity Index. Axis I and Axis II psychiatric disorders and personality disturbances were found to be more prevalent amongst pathological gamblers than other gamblers with the strongest differences observed for mood and anxiety-related disorders. Almost two-thirds of pathological gamblers reported both an anxiety or mood disorder in conjunction with another type of disorder. These differences between the gambling groups existed even after controlling for gender. The results highlight the high rates of co-morbidity in pathological gamblers in the community and the extent to which anxiety and mood disorders co-existing with other forms of pathology. These results highlight the significant challenges facing treatment services in the treatment of PG and the extent to which this should be treated as the primary disorder.     

Gray Markets,A Product of Demand Uncertainty and Excess Inventory

Diverting large quantities of goods from authorized distribution channels to unauthorized or “gray market” channels, albeit legal, significantly affects both firms and consumers due to effects on price, revenue, service and warranty availability, and product availability. In this paper we consider mechanisms by which the uncertainty surrounding inventory ordering decisions drives gray markets. We start with a minimal stochastic supply chain model composed of a producer and a retailer; then we restructure the model to add a distributor whereby the distributor and authorized retailer have the option of diverting inventory to a gray market. Our analysis sheds light on three issues: impacts of diversion on the various supply chain participants, strategies producers could use to combat or exploit gray markets, and important considerations for authorized retailers trying to set optimal order quantities in the presence of a gray market. Our analysis yields new insights into the behavior and impact of gray markets, which can inform management strategies and policies for confronting them.     

A Note on Asymptotic Behavior of the Nonparametric Density Estimators in Multivariate Mixtures

The asymptotic behavior of the nonparametric density estimator has been given for a multivariate mixture model. It has been observed that the estimator is asymptotically normally distributed with bias of size h ² and variance of size (nh)^?1.