The offender group reconviction scale: a statistical reconviction score for use by probation officers

The criminal courts in England and Wales may request the probation service to submit pre-sentence reports which are considered by magistrates and judges before making their sentencing decision. Pre-sentence reports must include an assessment of the risk of reoffending and the risk of harm to the public which the convicted offender presents. The offender group reconviction scale is a statistical aid to such risk assessment. We describe the scale and the statistical analysis on which it is based, and we discuss some statistical aspects of its interpretation and use.     

Forecasting obesity trends in England

Summary.  Forecasts of trends in obesity in England for 2010 are produced by treating the available data, which contain the proportions of the population, categorized by age and sex, falling into different body mass index ranges, as compositional data sets, so that the implicit simplex restrictions are automatically satisfied. Forecasts are calculated by using linear trend models for the log-ratio transformations and are accompanied by prediction regions. The advantages of treating data on proportions compositionally are emphasized and compared with forecasts that have been obtained by ignoring this restriction.     

Autoregressive Forecasting of Some Functional Climatic Variations

Many variations such as the annual cycle in sea surface temperatures can be considered to be smooth functions and are appropriately described using methods from functional data analysis. This study defines a class of functional autoregressive (FAR) models which can be used as robust predictors for making forecasts of entire smooth functions in the future. The methods are illustrated and compared with pointwise predictors such as SARIMA by applying them to forecasting the entire annual cycle of climatological El Nino–Southern Oscillation (ENSO) time series one year ahead. Forecasts for the period 1987–1996 suggest that the FAR functional predictors show some promising skill, compared to traditional scalar SARIMA forecasts which perform poorly.     

Forecasting Materials Damage from Air Pollution

Forecasts of materials damage from air pollution require (a) knowledge of a “damage function,” (b) an estimate of the exposure of materials to pollutants and other factors that enter into the dose relationship, and (c) the quantity and characteristics of the exposed materials. Uncertainty about any of these factors can lead to unacceptable uncertainty or bias in damage forecasts. Particular attention is given to the influence of the spatial units used to derive forecasts of materials damage. Environmental corrosion can vary significantly over relatively small geographic areas; accordingly, the choice of spatial unit can have an impact on forecasts of damage. The environmental factors that influence materials damage may not be independent over the time of exposure. There appears to be some evidence, for example, that SO₂ is not independent of time of wetness, both factors in metal corrosion.     

Combination forecasting for directional accuracy: An application to survey interest rate forecasts

Using published interest rates forecasts issued by professional economists, two combination forecasts designed to improve the directional accuracy of interest rate forecasting are constructed. The first combination forecast takes a weighted average of the individual forecasters' predictions. The more successful the forecaster was in past forecasts at predicting the direction of change in interest rates, the greater is the weight given to his/her current forecast. The second combination forecast is simply the forecast issued by the forecaster who had the greatest success rate at predicting the direction of change in interest rates in previous forecasts. In cases where two or more forecasters tie for best historic directional accuracy track record, the arithmetic mean of these forecasters is used. The study finds that neither combination forecasting method performs better than coin-flipping at predicting the direction of change in interest rates. Nor does either method beat the simple arithmetic mean of the predictions of all the forecasters surveyed at predicting the direction of change in interest rates.     

Sentencing convicted felons in the United States: a Bayesian analysis using multilevel covariates

Imprisonment levels vary widely across the United States, with some state imprisonment rates six times higher than others. Imposition of prison sentences also varies between counties within states, with previous research suggesting that covariates such as crime rate, unemployment level, racial composition, political conservatism, geographic region, and sentencing policies account for some of this variation. Other studies, using court data on individual felons, demonstrate how type of offense, demographics, criminal history, and case characteristics affect sentence severity. This article considers the effects of both county-level and individual-level covariates on whether a convicted felon receives a prison sentence rather than a jail or non-custodial sentence. We analyze felony court case processing data from May 1998 for 39 of the nation's most populous urban counties using a Bayesian hierarchical logistic regression model. By adopting a Bayesian approach, we are able to overcome a number of challenges. The model allows individual-level effects to vary by county, but relates these effects across counties using county-level covariates. We account for missing data using imputation via additional Gibbs sampling steps when estimating the model. Finally, we use posterior samples to construct novel predictor effect plots to aid communication of results to criminal justice policy-makers.     

Editorial Statement

A recently developed statistical model, called Bayesian vector autoregression, has proven to be a useful tool for economic forecasting. Such a model today forecasts a strong resurgence of growth in the second half of 1985 and in 1986.     

LOPSIDED REASONING ON LOPSIDED TESTS AND MULTIPLE COMPARISONS

For those who have not recognized the disparate natures of tests of statistical hypotheses and tests of scientific hypotheses, one‐tailed statistical tests of null hypotheses such as ?≤ 0 or ?≥ 0 have often seemed a reasonable procedure. We earlier reviewed the many grounds for not regarding them as such. To have at least some power for detection of effects in the unpredicted direction, several authors have independently proposed the use of lopsided (also termed split‐tailed, directed or one‐and‐a‐half‐tailed) tests, two‐tailed tests with α partitioned unequally between the two tails of the test statistic distribution. We review the history of these proposals and conclude that lopsided tests are never justified. They are based on the same misunderstandings that have led to massive misuse of one‐tailed tests as well as to much needless worry, for more than half a century, over the various so‐called ‘multiplicity problems’. We discuss from a neo‐Fisherian point of view the undesirable properties of multiple comparison procedures based on either (i) maximum potential set‐wise (or family‐wise) type I error rates (SWERs), or (ii) the increasingly fashionable, maximum potential false discovery rates (FDRs). Neither the classical nor the newer multiple comparison procedures based on fixed maximum potential set‐wise error rates are helpful to the cogent analysis and interpretation of scientific data.     

University student enrollment forecasts by analysis structural ratios using ARIMA-methods

Summary Forecasts for the number of students in Germany are conducted by the Kultusministerkonferenz. They use a transition model which does not allow for prediction intervals and therefore lack a measure of uncertainty of the forecast. Since the uncertainty is high for such forecasts, this lack is of importance. In this paper, structural ratios, relating the number of university students to the population of the same age, are analyzed and forescasted using ARIMA-models with outliers. Multiplying these ratios with official population forecasts for Germany provides the future number of students, additionally giving prediction intervals. This number will increase from 1.94 million in 2002 to 2.35 million in 2015. The uncertainty of the forecast is high; the forecast interval in 2015 will range between 1.72 and 2.98 million at a 95% confidence level. Supported by the German Research Foundation (DFG). We are grateful to an anonymous referee for some helpful comments.     

Combining probability forecasts

Summary.  Linear pooling is by far the most popular method for combining probability forecasts. However, any non-trivial weighted average of two or more distinct, calibrated probability forecasts is necessarily uncalibrated and lacks sharpness. In view of this, linear pooling requires recalibration, even in the ideal case in which the individual forecasts are calibrated. Towards this end, we propose a beta-transformed linear opinion pool for the aggregation of probability forecasts from distinct, calibrated or uncalibrated sources. The method fits an optimal non-linearly recalibrated forecast combination, by compositing a beta transform and the traditional linear opinion pool. The technique is illustrated in a simulation example and in a case-study on statistical and National Weather Service probability of precipitation forecasts.     

Methods for national population forecasts: a review

"Three widely used classes of methods for forecasting national populations are reviewed: demographic accounting/cohort-component methods for long-range projections, statistical time series methods for short-range forecasts, and structural modeling methods for the simulation and forecasting of the effects of policy changes. In each case, the major characteristics, strengths, and weaknesses of the methods are described. Factors that place intrinsic limits on the accuracy of population forecasts are articulated. Promising lines of additional research by statisticians and demographers are identified for each class of methods and for population forecasting generally."     

Testing the reliability of forecasting systems

The problem of statistically evaluating forecasting systems is revisited. The forecaster claims the forecasts to exhibit a certain nominal statistical behaviour; for instance, the forecasts provide the expected value (or certain quantiles) of the verification, conditional on the information available at forecast time. Forecasting systems that indeed exhibit the nominal behaviour are referred to as reliable. Statistical tests for reliability are presented (based on an archive of verification–forecast pairs). As noted previously, devising such tests is encumbered by the fact that the dependence structure of the verification–forecast pairs is not known in general. Ignoring this dependence though might lead to incorrect tests and too-frequent rejection of forecasting systems that are actually reliable. On the other hand, reliability typically implies that the forecast provides information about the dependence structure, and using this in conjunction with judicious choices of the test statistic, rigorous results on the asymptotic distribution of the test statistic are obtained. These results are used to test for reliability under minimal additional assumptions on the statistical properties of the verification–forecast pairs. Applications to environmental forecasts are discussed. A python implementation of the discussed methods is available online.     

Reflections on the cot death cases

Sally Clark, Angela Cannings and Trupti Patel not only suffered the tragic sudden deaths of two or more babies. They were then accused, and in the first two cases convicted, of their murder. The misuse of statistics at Sally Clark's trial hadprofound consequences, especially for her, but also for the subsequent cases. Ray Hill reflects on the statistical issues involved.     

Error models for official mortality forecasts

"The Office of the Actuary, U.S. Social Security Administration, produces alternative forecasts of mortality to reflect uncertainty about the future.... In this article we identify the components and assumptions of the official forecasts and approximate them by stochastic parametric models. We estimate parameters of the models from past data, derive statistical intervals for the forecasts, and compare them with the official high-low intervals. We use the models to evaluate the forecasts rather than to develop different predictions of the future. Analysis of data from 1972 to 1985 shows that the official intervals for mortality forecasts for males or females aged 45-70 have approximately a 95% chance of including the true mortality rate in any year. For other ages the chances are much less than 95%."     

On the directional accuracy of inflation forecasts: evidence from South African survey data

We study the information content of South African inflation survey data by determining the directional accuracy of both short-term and long-term forecasts. We use relative operating characteristic (ROC) curves, which have been applied in a variety of fields including weather forecasting and radiology, to ascertain the directional accuracy of the forecasts. A ROC curve summarizes the directional accuracy of forecasts by comparing the rate of true signals (sensitivity) with the rate of false signals (one minus specifity). A ROC curve goes beyond market-timing tests widely studied in earlier research as this comparison is carried out for many alternative values of a decision criterion that discriminates between signals (of a rising inflation rate) and nonsignals (of an unchanged or a falling inflation rate). We find consistent evidence that forecasts contain information with respect to the subsequent direction of change of the inflation rate.     

新时代发挥统计监督职能的思考

党的十九届四中全会将统计监督纳入国家监督体系之中。随着中国特色社会主义进入新时代以及大数据与人工智能技术的快速发展与应用,统计监督面对的内外部环境都发生了深刻的变化,如何在新环境下进一步发挥统计监督作用成为了一个十分重要的问题。本文系统梳理了有关统计监督的各种论述,对统计监督的内涵进行了重新认识,回顾了新中国成立以来我国统计监督的实践和做法,并与国外统计监督进行了比较,最后提出了在新时代进一步发挥统计监督职能的若干建议。     

Statistical models for short- and long-term forecasts of snow depth

Forecasting of future snow depths is useful for many applications like road safety, winter sport activities, avalanche risk assessment and hydrology. Motivated by the lack of statistical forecasts models for snow depth, in this paper we present a set of models to fill this gap. First, we present a model to do short-term forecasts when we assume that reliable weather forecasts of air temperature and precipitation are available. The covariates are included nonlinearly into the model following basic physical principles of snowfall, snow aging and melting. Due to the large set of observations with snow depth equal to zero, we use a zero-inflated gamma regression model, which is commonly used to similar applications like precipitation. We also do long-term forecasts of snow depth and much further than traditional weather forecasts for temperature and precipitation. The long-term forecasts are based on fitting models to historic time series of precipitation, temperature and snow depth. We fit the models to data from six locations in Norway with different climatic and vegetation properties. Forecasting five days into the future, the results showed that, given reliable weather forecasts of temperature and precipitation, the forecast errors in absolute value was between 3 and 7?cm for different locations in Norway. Forecasting three weeks into the future, the forecast errors were between 7 and 16?cm.     

The Role of Statistical Models in the Prediction of Tropical Cyclone Motion

Tropical cyclones (tropical storms, hurricanes, typhoons, etc.) occur over many of the earth's tropical marine areas. Responsibility for tracking and predicting the future course of these storms is assigned to one or more domestic or foreign meteorological services. These services routinely activate a number of statistical and dynamical prediction models as objective guidance preparatory to issuing official forecasts on these storms. In this article, the role of the statistical models in this process is examined.     

Population projections--a forecast or a perspective calculation

The author argues that population projections should not be treated as forecasts but as provisional calculations based on certain known or assumed relationships. Therefore, when these relationships change, so should the projections that are based on them. It is suggested that many users think of population projections as forecasts, which often do not materialize because the assumptions on which they are based turn out to be invalid. The policy implications of such a distinction are considered.     

金融监管效应的统计评价方法

一、复杂性理论概述1984年,诺贝尔物理学奖获得者盖尔曼和安德逊、经济学奖获得者阿罗等人成立了桑塔费研究所(SFI),专门从事复杂科学的研究,这一般认为是复杂科学研究的开端。复杂性科学研究的复杂系统涉及范围广泛,涵盖了工程、生物、管理、经济等各个方面。1999年,美国《科学》杂志出版了“复杂系统”专刊,我国自1999年起,国家自然科学基金会也专门设立了复杂性科学研究的专项基金,复杂性理论得到了国内外的广泛重视。复杂性理论对金融监管效应统计评价的启发主要是在经济研究范式的转换,在复杂系统中所涉及的一些基本特征,如:非线性、…