Using Neural Networks to Model the Behavior and Decisions of Gamblers,in Particular,Cyber-Gamblers

This article describes the use of neural networks (a type of artificial intelligence) and an empirical data sample of, inter alia, the amounts of bets laid and the winnings/losses made in successive games by a number of cyber-gamblers to longitudinally model gamblers’ behavior and decisions as to such bet amounts and the temporal trajectory of winnings/losses. The data was collected by videoing Texas Holdem gamblers at a cyber-gambling website. Six “persistent” gamblers were identified, totaling 675 games. The neural networks on average were able to predict bet amounts and cumulative winnings/losses in successive games accurately to three decimal places of the dollar. A more important conclusion is that the influence of a gambler’s skills, strategies, and personality on his/her successive bet amounts and cumulative winnings/losses is almost totally reflected by the pattern(s) of his/her winnings/losses in the few initial games and his/her gambling account balance. This partially invalidates gamblers’ illusions and fallacies that they can outperform others or even bankers. For government policy-makers, gambling industry operators, economists, sociologists, psychiatrists, and psychologists, this article provides models for gamblers’ behavior and decisions. It also explores and exemplifies the usefulness of neural networks and artificial intelligence at large in the research on gambling.     

Nonparametric Instrumental Regression

The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ defined by conditional moment restrictions that stem from a structural econometric model E[Y−ϕ(Z)|W]=0, and involve endogenous variables Y and Z and instruments W. The function ϕ is the solution of an ill‐posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyzes identification and overidentification of this model, and presents asymptotic properties of the estimated nonparametric instrumental regression function.     

证人出庭与刑事被告人对质权的保障(英文)

刑事证人出庭作证的主要意义在于保障被告人的对质权。因此,刑事证人出庭问题基本上可替换为以被告人对质权为核心的必要证人出庭问题。由于中国的刑事诉讼法及其司法解释并未赋予被告人对质权,实践中必要证人出庭率仅为25%左右。这种无对质权语境下的刑事审判,通常表现为积极的实体真实主义、定罪量刑的非精细化操作和裁判正当性来源的单一化。为改变这一局面,有必要确立对质权制度及相应的保障机制。     

We care about care: advice by children in care for children in care,foster parents and child welfare workers about the transition into foster care

Twenty children in foster care, ages 8 to 15 years, provided advice to children in care, foster parents and child welfare workers about ways to assist service delivery during the transition into foster care. The children discussed the importance of tending to experiences such as foster home expectations, the importance of time and information, the new foster/parent–child relationship, coping with stress, the ability to be engaged in decision‐making, the benefits of foster care and the need to build a trusting and personal relationship between children in care and their caregivers. The importance of listening to children's experiences of the transition into foster care and incorporating their advice into future research, policy and practice will be discussed.     

Piecewise proportional hazards models with interval-censored data

We consider the piecewise proportional hazards (PWPH) model with interval-censored (IC) relapse times under the distribution-free set-up. The partial likelihood approach is not applicable for IC data, and the generalized likelihood approach has not been studied in the literature. It turns out that under the PWPH model with IC data, the semi-parametric MLE (SMLE) of the covariate effect under the standard generalized likelihood may not be unique and may not be consistent. In fact, the parameter under the PWPH model with IC data is not identifiable unless the identifiability assumption is imposed. We propose a modification to the likelihood function so that its SMLE is unique. Under the identifiability assumption, our simulation study suggests that the SMLE is consistent. We apply the method to our cancer relapse time data and conclude that the bone marrow micrometastasis does not have a significant prognostic factor.     

Modern variable selection for longitudinal semi-parametric models with missing data

Penalized methods for variable selection such as the Smoothly Clipped Absolute Deviation penalty have been increasingly applied to aid variable section in regression analysis. Much of the literature has focused on parametric models, while a few recent studies have shifted the focus and developed their applications for the popular semi-parametric, or distribution-free, generalized estimating equations (GEEs) and weighted GEE (WGEE). However, although the WGEE is composed of one main and one missing-data module, available methods only focus on the main module, with no variable selection for the missing-data module. In this paper, we develop a new approach to further extend the existing methods to enable variable selection for both modules. The approach is illustrated by both real and simulated study data.     

A review of backtesting for value at risk

There have been many backtesting methods proposed for value at risk. Yet they have rarely been applied in practice. Here, we provide a comprehensive review of all of the recent backtesting methods for VaR. This review could encourage applications and also the development of further backtesting methods.     

Assessing a minimum pension guarantee for the voluntary IPS in Turkey

The returns from individual account pension plans are subject to fluctuations in capital markets. This increases income uncertainty for the beneficiary and exposes individuals to the risk of fluctuations in the economy in general and the stock market in particular. A minimum pension guarantee is a way to avoid this pitfall by providing a minimum annuity regardless of the actual investment performance of individual accounts. In this article, we present a cost analysis of a minimum benefit guarantee mechanism for the voluntary Individual Pension System in Turkey. We examine the cost estimates and the probability of providing guaranteed payments under various economic and demographic assumptions.     

Likelihood estimation for longitudinal zero-inflated power series regression models

In this paper, a zero-inflated power series regression model for longitudinal count data with excess zeros is presented. We demonstrate how to calculate the likelihood for such data when it is assumed that the increment in the cumulative total follows a discrete distribution with a location parameter that depends on a linear function of explanatory variables. Simulation studies indicate that this method can provide improvements in obtaining standard errors of the estimates. We also calculate the dispersion index for this model. The influence of a small perturbation of the dispersion index of the zero-inflated model on likelihood displacement is also studied. The zero-inflated negative binomial regression model is illustrated on data regarding joint damage in psoriatic arthritis.     

Marginal methods for clustered longitudinal binary data with incomplete covariates

Many analyses for incomplete longitudinal data are directed to examining the impact of covariates on the marginal mean responses. We consider the setting in which longitudinal responses are collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean and association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters when covariates are missing at random. Empirical studies demonstrate that estimators from the proposed method have negligible finite sample biases in moderate samples. An application to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) demonstrates the utility of the proposed method.