In this paper we review the state space approach to time series analysis and establish the notation that is adopted in this special volume of the Journal of Statistical Software. We first provide some background on the history of state space methods for the analysis of time series. This is followed by a concise overview of linear Gaussian state space analysis including the modelling framework and appropriate estimation methods. We discuss the important class of unobserved component models which incorporate a trend, a seasonal, a cycle, and fixed explanatory and intervention variables for the univariate and multivariate analysis of time series. We continue the discussion by presenting methods for the computation of different estimates for the unobserved state vector: filtering, prediction, and smoothing. Estimation approaches for the other parameters in the model are also considered. Next, we discuss how the estimation procedures can be used for constructing confidence intervals, detecting outlier observations and structural breaks, and testing model assumptions of residual independence, homoscedasticity, and normality. We then show how ARIMA and ARIMA components models fit in the state space framework to time series analysis. We also provide a basic introduction for non-Gaussian state space models. Finally, we present an overview of the software tools currently available for the analysis of time series with state space methods as they are discussed in the other contributions to this special volume.Keywords: ARMA model, Kalman filter, state space methods, unobserved components, software tools. Promising trendsIn 2001, when considering the possible drawbacks of state space models, Durbin and Koopman wrote: "In our opinion, the only disadvantages are the relative lack in the statistical and econometric communities of information, knowledge, and software regarding these models.", see Durbin and Koopman (2001, p. 52). Ten years later, it is gratifying to see how much progress has been made in the further dissemination of these methods. Not only have state 2 Statistical Software for State Space Methods space models been applied in a growing number of scientific fields, but -as is witnessed by this special volume of the Journal of Statistical Software that is completely dedicated to statistical software for state space methods -they have been implemented in STAMP, R, MATLAB, REGCMPNT, SAS, EViews, GAUSS, Stata, RATS, gretl, and SsfPack with links established with S-PLUS and Ox.State space methods originated in the field of control engineering, starting with the groundbreaking paper of Kalman (1960). They were initially (and still are) deployed for the purpose of accurately tracking the position and velocity of moving objects such as ships, airplanes, missiles, and rockets. A riveting account of this application of state space methods to space travel can be found in the NASA (National Aeronautics and Space Administration) report of McGee and Schmidt (1985), a document that has clearly been written on an old-fash...

Cyclists may have incorrect expectations of the behaviour of automated vehicles in interactions with them, which could bring extra risks in traffic. This study investigated whether expectations and behavioural intentions of cyclists when interacting with automated cars differed from those with manually driven cars. A photo experiment was conducted with 35 participants who judged bicycle-car interactions from the perspective of the cyclist. Thirty photos were presented. An experimental design was used with between-subjects factor instruction (two levels: positive, neutral), and two within-subjects factors: car type (three levels: roof name plate, stickerthese two external features indicated automated cars; and traditional car), and series (two levels: first, second). Participants were asked how sure they were to be noticed by the car shown in the photos, whether the car would stop, and how they would behave themselves. A subset of nine participants was equipped with an eye-tracker. Findings generally point to cautious dispositions towards automated cars: participants were not more confident to be noticed when interacting with both types of automated cars than with manually driven cars. Participants were more confident that automated cars would stop for them during the second series and looked significantly longer at automated cars during the first.

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ObjectiveImplantable cardioverter defibrillators (ICD) and cardiac resynchronisation therapy (CRT) have substantially improved the survival of patients with cardiomyopathy. Eligibility for this therapy requires a left ventricular ejection fraction (LVEF) <35 %. This is largely based on studies using echocardiography. Cardiac magnetic resonance imaging (CMR) is increasingly utilised for LVEF assessment, but several studies have shown differences between LVEF assessed by CMR and echocardiography. The present study compared LVEF assessment by CMR and echocardiography in a heart failure population and evaluated effects on eligibility for device therapy.Methods152 patients (106 male, mean age 65.5 ± 9.9 years) referred for device therapy were included. During evaluation of eligibility they underwent both CMR and echocardiographic LVEF assessment. CMR volumes were computed from a stack of short-axis images. Echocardiographic volumes were computed using Simpson’s biplane method.ResultsThe study population demonstrated an underestimation of end-diastolic volume (EDV) and end-systolic volume (ESV) by echocardiography of 71 ± 53 ml (mean ± SD) and 70 ± 49 ml, respectively. This resulted in an overestimation of LVEF of 6.6 ± 8.3 % by echocardiography compared with CMR (echocardiographic LVEF 31.5 ± 8.7 % and CMR LVEF 24.9 ± 9.6 %). 28 % of patients had opposing outcomes of eligibility for cardiac device therapy depending on the imaging modality used.ConclusionWe found EDV and ESV to be underestimated by echocardiography, and LVEF assessed by CMR to be significantly smaller than by echocardiography. Applying an LVEF cut-off value of 35 %, CMR would significantly increase the number of patients eligible for device implantation. Therefore, LVEF cut-off values might need reassessment when using CMR.

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Listening to music or talking on the phone while cycling as well as the growing number of quiet (electric) cars on the road can make the use of auditory cues challenging for cyclists. The present study examined to what extent and in which traffic situations traffic sounds are important for safe cycling. Furthermore, the study investigated the potential safety implications of limited auditory information caused by quiet (electric) cars and by cyclists listening to music or talking on the phone. An Internet survey among 2249 cyclists in three age groups (16-18, 30-40 and 65-70year old) was carried out to collect information on the following aspects: 1) the auditory perception of traffic sounds, including the sounds of quiet (electric) cars; 2) the possible compensatory behaviours of cyclists who listen to music or talk on their mobile phones; 3) the possible contribution of listening to music and talking on the phone to cycling crashes and incidents. Age differences with respect to those three aspects were analysed. Results show that listening to music and talking on the phone negatively affects perception of sounds crucial for safe cycling. However, taking into account the influence of confounding variables, no relationship was found between the frequency of listening to music or talking on the phone and the frequency of incidents among teenage cyclists. This may be due to cyclists' compensating for the use of portable devices. Listening to music or talking on the phone whilst cycling may still pose a risk in the absence of compensatory behaviour or in a traffic environment with less extensive and less safe cycling infrastructure than the Dutch setting. With the increasing number of quiet (electric) cars on the road, cyclists in the future may also need to compensate for the limited auditory input of these cars.

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