Sexual Victimization and Sex-Related Drinking Motives: How Protective is Emotion Regulation?

One in five college women experience sexual victimization (SV), and SV severity is associated with subsequent psychological distress, including sex-related distress. SV severity may also be associated with drinking motives to cope with sex-related distress and to enhance sex (sex-related drinking motives [SRDMs]), particularly if individuals suffer from emotion regulation (ER) difficulties. College women (N = 151) completed a survey assessment of ER, SV history, childhood sexual abuse (CSA), and SRDMs. Twelve regression models assessed six facets of ER as moderators between SV severity and SRDMs. Among women with no or low levels of prior SV severity, women with greater access to ER strategies were less likely to endorse drinking to cope SRDMs. At higher levels of SV severity, women at all levels of access to ER strategies were equally likely to endorse drinking to cope SRDMs, suggesting that access to ER strategies did not mitigate motivations to drink to cope with sex-related distress for these women. Women with severe SV histories may benefit from interventions that build on existing ER strengths or address other factors. However, greater access to ER strategies may serve as a protective factor against SRDMs when SV severity is low.     

NONPARAMETRIC QUANTILE ESTIMATION FROM RECORD-BREAKING DATA

Sometimes, in industrial quality control experiments and destructive stress testing, only values smaller than all previous ones are observed. Here we consider nonparametric quantile estimation, both the ‘sample quantile function’ and kernel-type estimators, from such record-breaking data. For a single record-breaking sample, consistent estimation is not possible except in the extreme tails of the distribution. Hence replication is required, and for m. such independent record-breaking samples the quantile estimators are shown to be strongly consistent and asymptotically normal as m-→∞. Also, for small m, the mean-squared errors, biases and smoothing parameters (for the smoothed estimators) are investigated through computer simulations.     

EFFECT OF CAPITAL IMPORTS ON SAVINGS AND GROWTH IN LESS DEVELOPED COUNTRIES

This paper examines some of the arguments of the critics of foreign aid and other capital inflows to less developed countries (LDCs). The paper finds that the critics lack sufficient evidence on the supposedly adverse effect of capital transfers to LDCs on their savings and growth of incomes. This, however, does not mean that these capital inflows always promote growth in LDCs. In particular, it is shown that the relative importance of foreign capital on economic growth of LDCs would depend on the degree to which that growth is constrained by the lack of capital.     

Families of smooth confidence bands for the survival function under the general random censorship model

Randomly right censored data often arise in industrial life testing and clinical trials. Several authors have proposed asymptotic confidence bands for the survival function when data are randomly censored on the right. All of these bands are based on the empirical estimator of the survival function. In this paper, families of asymptotic (1-)100% level confidence bands are developed from the smoothed estimate of the survival function under the general random censorship model. The new bands are compared to empirical bands, and it is shown that for small sample sizes, the smooth bands have a higher coverage probability than the empirical counterparts.     

Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model

As an environmental phenomenon, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Although a number of commercial loss projection models have been developed to predict the property losses, only a handful of studies are available in the public domain to predict damage for hurricane prone areas. The state of Florida has developed an open, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes. The model comprises three components; viz. the atmospheric science component, the engineering component and the actuarial science component. The atmospheric component includes modeling the track and intensity life cycle of each simulated hurricane within the Florida threat area. Based on historical hurricane statistics, thousands of storms are simulated allowing determination of the wind risk for all residential Zip Code locations in Florida. The wind risk information is then provided to the engineering and actuarial components to model damage and average annual loss, respectively. The actuarial team finds the county-wise loss and the total loss for the entire state of Florida. The computer team then compiles all information from atmospheric science, engineering and actuarial components, processes all hurricane related data and completes the project. The model was submitted to the Florida Commission on Hurricane Loss Projection Methodology for approval and went through a rigorous review and was revised as per the suggestions of the commission. The final model was approved for use by the insurance companies in Florida by the commission. At every stage of the process, statistical procedures were used to model various parameters and validate the model. This paper presents a brief summary of the main components of the model (meteorology, vulnerability and actuarial) and then focuses on the statistical validation of the same.     

Authors’ responses to the discussion on “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Model”

Hurricanes threaten the Florida coast line every year and are capable of causing catastrophic losses. The Public Hurricane Loss Model was developed in response to the need for having an open transparent model to predict the above losses. The results were summarized in the paper “Predicting Losses of Residential Structures in the State of Florida by the Public Hurricane Loss Model” which was subsequently a subject of discussion by experts in Meteorology, Engineering, Actuarial Sciences and Statistics. The present paper presents a response to the discussions on the above mentioned article.     

A Model-Based Approach to Investigate Performance Improvements in Rule-Based Expert Systems

One of the major issues in the development of large, rule-based expert systems is related to improving their performance efficiency. One way to address this issue is by reducing the number of unsuccessful tries a system goes through before executing a rule to establish a goal or an intermediary fact. On the average, the number of unsuccessful tries can be reduced if the rules that are tried first are those that are expected to execute most frequently, and this can be established by extracting information on the probability distributions of the input parameters. In this paper, a rule base is modeled as a network and simulated to investigate potential performance improvements by changing the order used to test the rules. The model of the rule base is also used to investigate performance gains achieved by parameter factorization and premise clause reordering.     

Nonparametric function estimation from inversely sampled record-breaking data

Often, in industrial stress testing, meteorological data analysis, and other similar situations, measurements may be made sequentially and only values smaller than all previous ones are recorded. When the number of records is fixed in advance, the data are referred to as inversely sampled record-breaking data. This paper is concerned with nonparametric estimation of the distribution and density functions from such data (successive minima). For a single record-breaking sample, consistent estimation is not possible except in the extreme left tail of the distribution. Hence, replication is required, and for m such independent record-breaking samples, the estimators are shown to be strongly consistent and asymptotically normal as m ∞ →. Computer simulations are used to investigate the effect of the bandwidth on the mean squared errors and biases of the smooth estimators, and are also used to provide a comparison of their performance with the analogous estimators obtained under random sampling for record values.     

Statistical Control for Road Pavements

Roads are assets vital to the economies of nations, particularly those with such low population density as Australia and New Zealand. The quality of road construction is of great importance. The application of statistical techniques to determining whether the construction of road pavement is being carried out to the design specification can be of great benefit to administrators and contractors. In this paper, a method is presented for obtaining control limits for a means chart when correlated observations are taken over a grid on a two‐dimensional surface. Data from a closely monitored road construction project are used to determine if the construction process can be considered ‘in control’. The nature of the correlation structure is determined using ARIMA models. The ultimate aim of the control charts and specifications is to control base course thickness. Controlling the pavement surfaces is a means to this end.