Statistical Software for State Space Methods

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.

Comparison of analytical methods for detection of perchloroethylene glutathione conjugates and application in liver fractions

Perchloroethylene (PER) is a degreasing solvent widelyused to replace the toxic trichloroethylene. However, PERcan also lead to nephrotoxicity via bioactivation by theglutathione (GSH) conjugative pathway. In this study, apostcolumn o-phtaldialdehyde (OPA)/N-acetylcysteine(NAC) derivatisation method has been developed andcompared to existing methods for analysis of PER GSHconjugates. Subsequently, the rate of this conjugation hasbeen studied for the first time in human subcellularfractions. The specific activity of PER GSH conjugation inhumans shows significant interindividual differences andis 10-fold lower compared to rats, indicating that humansare less susceptible to nephrotoxicity via this pathway

Hematite coated, conductive Y doped ZnO nanorods for high efficiency solar water splitting

For the first time, hematite (α-Fe2O3) crystals were electrochemically deposited over vertically aligned conductive zinc oxide nanorods (NR) to form a specially designed 3D heterostructure with a unique triple layer structure. The structure formed with a thin layer of ZnFe2O4 sandwiched between the hematite and the ZnO, which forms a barrier to reduce the back migration of holes. Hence, the charge separation is significantly improved. The small unequal bandgaps of α-Fe2O3 and ZnFe2O4 help to enhance and broaden visible light absorption. The electron transportation was further improved by yttrium doping in the ZnO (YZnO) NRs, resulting in increased conductivity. This allowed the vertically aligned NRs to perform as electron highways, which also behave as effective optical waveguides for improved light trapping and absorption, since ZnO absorbs little visible light. All these benefits made the unique structures suitable for high performance photoelectrochemical (PEC) water splitting. Optimisation of α-Fe2O3 thickness led to a photocurrent density improvement from 0.66 to 0.95 mA cm−2 at 1.23 VRHE. This was further improved to 1.59 mA cm−2 by annealing at 550 °C for 3 h, representing a record-breaking photocurrent for α-Fe2O3/ZnO systems. Finally IPCE confirmed the successful generation and transfer of photoelectrons under visible light excitation in the specifically designed heterostructure photoanode, with 5% efficiency for blue light, and 15% for violet light

Assessment of left ventricular ejection fraction in patients eligible for ICD therapy: Discrepancy between cardiac magnetic resonance imaging and 2D echocardiography

OBJECTIVE: Implantable 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. METHODS: 152 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. RESULTS: The 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. CONCLUSION: We 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

Time Series Modelling with MATLAB: the SSpace toolbox

SSpace is a MATLAB toolbox for State-Space modeling that provides the user with tools for linear Gaussian, nonlinear, and non-Gaussian systems with the most advanced and up-to-date features available in any State-Space framework. Great flexibility is achieved because each model is coded on a standard MATLAB function, thence having absolute control on particular parameterizations, parameter constraints, time variation of parameters or variances, arbitrary nonlinear relations with inputs, time aggregation, nested models, system concatenation, etc. The toolbox may be used by specifying State-Space systems from scratch or by using ready-to-use templates for standard methods (like VARMAX, exponential smoothing, unobserved components, Dynamic Linear Regression, etc.). The toolbox is freely available via a public code repository with full documentation and help system. This chapter demonstrates the toolbox’s potential with several examples