No Arabic abstract
This study has investigated the mortality rate of parties at real estate auctions compared to that of the overall population in South Korea by using various variables, including age, real estate usage, cumulative number of real estate auction events, disposal of real estate, and appraisal price. In each case, there has been a significant difference between mortality rate of parties at real estate auctions and that of the overall population, which provides a new insight regarding utilization of the information on real estate auctions. Despite the need for further detailed analysis on the correlation between real estate auction events and death, because the result from this study is still meaningful, the result is summarized for informational purposes.
Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In this paper we propose a two-stage regression model that exploits the textual data in real estate classifieds. We show how our model can be used to predict the price of a real estate classified. We also show how our model can be used to highlight keywords that affect the price positively or negatively. To assess our contributions, we analyze four real world data sets, which we gathered from three different property websites. The analysis shows that our model (which exploits textual features) achieves significantly lower root mean squared error across the different data sets and against variety of regression models.
The common fisheries policy aims at eliminating discarding which has been part of fisheries for centuries. It is important to monitor the compliance with the new regulations but estimating the discard rate is a challenging task, especially where the practise is illegal. The aim of this study was to review a length-based method that has been used to estimate the discard rate in Icelandic waters and explore the effects of different monitoring schemes. The length-based method estimates the minimum discard rate and the method of bootstrapping can be used to determine the uncertainty of the estimate. This study showed that the number of ships is the most important factor to consider in order to decrease the uncertainty.
Reliable mortality estimates at the subnational level are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations, where the stochastic variation in death counts is relatively high, and so the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when the model is applied to both simulated data which mimic US counties, and real data for French departments. The estimates produced by the model have direct applications to the study of subregional health patterns and disparities.
Forecasts of mortality provide vital information about future populations, with implications for pension and health-care policy as well as for decisions made by private companies about life insurance and annuity pricing. Stochastic mortality forecasts allow the uncertainty in mortality predictions to be taken into consideration when making policy decisions and setting product prices. Longer lifespans imply that forecasts of mortality at ages 90 and above will become more important in such calculations. This paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a Generalised Additive Model (GAM) for mortality for the majority of the age-range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age-specific improvement rates, together with a non-smoothed period effect. Forecasts for the United Kingdom are produced using data from the Human Mortality Database spanning the period 1961-2013. A metric that approximates predictive accuracy under Leave-One-Out cross-validation is used to estimate weights for the `stacking of forecasts with different points of transition between the GAM and parametric elements. Mortality for males and females are estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes, and furthermore provides for forecasts accounting for correlations in period innovations. The joint and single sex model forecasts estimated using data from 1961-2003 are compared against observed data from 2004-2013 to facilitate model assessment.
Forecasting accuracy of mortality data is important for the management of pension funds and pricing of life insurance in actuarial science. Age-specific mortality forecasting in the US poses a challenging problem in high dimensional time series analysis. Prior attempts utilize traditional dimension reduction techniques to avoid the curse of dimensionality, and then mortality forecasting is achieved through features forecasting. However, a method of reducing dimension pertinent to ideal forecasting is elusive. To address this, we propose a novel approach to pursue features that are not only capable of representing original data well but also capturing time-serial dependence as most as possible. The proposed method is adaptive for the US mortality data and enjoys good statistical performance. As a comparison, our method performs better than existing approaches, especially in regard to the Lee-Carter Model as a benchmark in mortality analysis. Based on forecasting results, we generate more accurate estimates of future life expectancies and prices of life annuities, which can have great financial impact on life insurers and social securities compared with using Lee-Carter Model. Furthermore, various simulations illustrate scenarios under which our method has advantages, as well as interpretation of the good performance on mortality data.