Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model

<p>To account for uncertainty in numerical weather prediction (NWP) models it has become common practice to employ ensembles of NWP forecasts. However, forecast ensembles often exhibit forecast biases and dispersion errors, thus require statistical postprocessing to improve reliability of the ensemble forecasts.<br>This work proposes an extension of a recently developed postprocessing model for temperature utilizing autoregressive information present in the forecast error of the raw ensemble members. The original approach is modified to let the variance parameter additionally depend on the ensemble spread, yielding a two-fold heteroscedastic model. Furthermore, a high-resolution forecast is included into the postprocessing model, yielding improved predictive performance. Finally, it is outlined how the autoregressive model can be utilized to postprocess ensemble forecasts with higher forecast horizons, without the necessity of making fundamental changes to the original model. To illustrate the performance of the heteroscedastic extension of the autoregressive model, and its use for higher forecast horizons we present a case study for a data set containing 12 years of temperature forecasts and observations over Germany. The case study indicates that the autoregressive model yields particularly strong improvements for forecast horizons beyond 24 hours ahead.</p>

Analisis Volatilitas Harga Daging Sapi Sebelum Sampai Dengan Sesudah Hari Besar Agama di Kota Jambi

This study aims to analyze the level of beef price volatility before fasting (D-7) to after Eid (H + 7) in Jambi City, and compile a forecast model. This study used a survey method for beef traders in the Angso Duo market, Jambi City. The analysis used to calculate the volatility of beef prices is the ARCH (Autoregressive Conditional Heteroscedastic) model analysis and the GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model analysis. The average price of beef during the period before fasting (D-7) to after Eid (H + 7) in Jambi City was IDR 124,147 per kg with the highest price of IDR 150,000 and the lowest was 110,000 per kg. The volatility of beef prices during the period before fasting (D-7) to after Eid (H + 7) in Jambi City is the highest before Eid al-Fitr (Eid). ARCH and GARCH models can predict the future value of beef prices.

Analysis of Volatility Volume and Open Interest for Nifty Index Futures Using GARCH Analysis and VAR Model

The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded on the National Stock Exchange of India, and the extent and direction of these relationships. A complete absence of bidirectional causality in any particular instance depicts noise trading and empirical analysis according to this study establishes that volume has a stronger impact on volatility compared to open interest. Furthermore, the impulse originating from volatility of volume and open interest is low.

Bootstrapping M-estimators in generalized autoregressive conditional heteroscedastic models

Summary We consider the weighted bootstrap approximation to the distribution of a class of M-estimators for the parameters of the generalized autoregressive conditional heteroscedastic model. We prove that the bootstrap distribution, given the data, is a consistent estimate in probability of the distribution of the M-estimator, which is asymptotically normal. We propose an algorithm for the computation of M-estimates which at the same time is useful for computing bootstrap replicates from the given data. Our simulation study indicates superior coverage rates for various weighted bootstrap schemes compared with the rates based on the normal approximation and existing bootstrap methods for the generalized autoregressive conditional heteroscedastic model, such as percentile $t$-subsampling schemes. Since some familiar bootstrap schemes are special cases of the weighted bootstrap, this paper thus provides a unified theory and algorithm for bootstrapping in generalized autoregressive conditional heteroscedastic models.

From regression function to diffusion drift estimation in nonparametric setting

We consider a diffusion model dXt = b(Xt)dt + σ(Xt)dWt,X0 = η, under conditions ensuring existence, stationarity and geometrical β-mixing of the process solution. We assume that we observe a sample (XkΔ)0≤k≤n+1. Our aim is to study nonparametric estimators of the drift function b(.), under general conditions. We propose projection estimators based on a least-squares type contrast and, in order to generalize existing results, we want to consider possibly non compactly supported projection bases and possibly non bounded volatility. To that aim, we relate the model with a simpler regression model, then to a more elaborate heteroscedastic model, plus some residual terms. This allows to see the role of heteroscedasticity first and the role of dependency between the variables and to present different probabilistic tools used to face each part of the problem. For each step, we try to see the “price” of each assumption. This is the developed version of the talk given in August 2018 in Dijon, Journées MAS.

Non-surgical Management of Large Periapical Lesions with the Agreement Evaluation of the Methods

Aim: The aim of this study is to evaluate the agreement between three routinely used non-surgical management techniques for large periapical lesions namely the treatments with Calcium hydroxide, Mineralo-Trioxide Aggregate and Bio-dentine. Methods: Data was collected from 60 patients at the Department of Restorative Dentistry, Faculty of Dental Sciences, University of Peradeniya. The variables age, gender and area of the infected region before and after the treatment and the treatment type were considered. Two homoscedastic and heteroscedastic Mixed-effects models were fitted and the agreement between three treatments were assessed using Concordance Correlation Coefficient (CCC) and Total Deviation Index (TDI). Results: CCC value calculated for treatment types 1 & 2, 1 & 3 and 2 & 3 are (0.905, 0.909, 0.874) for homoscedastic model and (0.989, 0.990, 0.975) for heteroscedastic model. Further, corresponding TDI values for homoscedastic and heteroscedastic models are (3.148, 4.390, 1.647) and (2.963, 4.388, 1.457) respectively. Conclusions: Since all the CCC values are close to 1 and TDI values are low, there is a strong agreement between all three treatments and hence they be used interchangeably. Moreover, the agreement between Treatments with Calcium hydroxide and Bio-dentine is higher compared to the agreements between the other treatments. (i.e., Calcium hydroxide with Mineralo-Trioxide Aggregate and Biodentine with Mineralo-Trioxide Aggregate).