277 research outputs found
Likelihood, Replicability, and Robbins' Confidence Sequences
The widely claimed replicability crisis in science may lead to revised standards of significance. The customary frequentist confidence intervals, calibrated through hypothetical repetitions of the experiment that is supposed to have produced the data at hand, rely on a feeble concept of replica- bility. In particular, contradictory conclusions may be reached when a substantial enlargement of the study is undertaken. To redefine statistical confidence in such a way that inferential conclusions are non-contradictory, with large enough probability, under enlargements of the sample, we give a new reading of a proposal dating back to the 60s, namely, Robbins\u2019 confidence sequences. Directly bounding the probability of reaching, in the future, conclusions that contradict the current ones, Robbins\u2019 confidence sequences ensure a clear-cut form of replicability when inference is performed on accumulating data. Their main frequentist property is easy to understand and to prove. We show that Robbins\u2019 confidence sequences may be justified under various views of inference: they are likelihood-based, can incorporate prior information and obey the strong likelihood principle. They are easy to compute, even when inference is on a parameter of interest, especially using a closed form approximation from normal asymptotic theory
On a likelihood interpretation of adjusted profile likelihoods through refined predictive densities.
In this paper a second-order link between adjusted profile likelihoods and refinements of the estimative predictive density is shown. The result provides a new straightforward interpretation for modified profile likelihoods, that complements results in Severini (1998a) and in Pace and Salvan (2006). Moreover, it outlines a form of consistency to second order between likelihood theory and prediction in frequentist inference
A Neyman-Scott phenomenon in model discrimination.
The aim of this paper is to show through simulation that a Neyman-Scott phenomenon may occur in discriminating among separate stratified models. We focus on models which are scale families in each stratum. We consider traditional model selection procedures, such as the Akaike and Takeuchi information criteria, together with procedures based on the marginal likelihood and its Laplace approximation. We perform two simulation studies. Results indicate that, when the sample size in each stratum is fixed and the number of strata increases, correct selection probabilities for traditional model selection criteria may approach zero. On the other hand, model selection based on exact or approximate marginal likelihoods, that exploit invariance, can behave far better
Likelihood theory, prediction, model selection: asymptotic connections.
Plug-in estimation and corresponding refinements involving penalisation have been considered in various areas of parametric statistical inference. One major example is adjustment of the profile likelihood for inference in the presence of nuisance parameters. Another important setting is prediction, where improved estimative predictive densities have been recently developed. A third related setting is model selection, where information criteria based on penalisation of maximised likelihood have been proposed starting from the pioneering contribution of Akaike. The seminal contributions in the last setting predate those introducing the former two classes of procedures, and pertinent portions of literature seem to have evolved quite independently. The aim of this paper is to establish some simple asymptotic connections among these classes of procedures. In particular, all the three kinds of penalisations involved can be viewed as bias corrections of plug-in estimates of theoretical target criteria which are shown to be very closely connected. As a by-product, we obtain adjusted profile likelihoods from optimal predictive densities. Links between adjusted procedures in likelihood theory and model selection procedures are also briefly enquired throuh some simulation studies
Efficient composite likelihood for a scalar parameter of interest
For inference in complex models, composite likelihood combines genuine likelihoods based on
Q3
low-dimensional portions of the data, with weights to be chosen. Optimal weights in composite
likelihood may be searched following different routes, leading to a solution only in scalar
parametermodels. Here, after briefly reviewing themain approaches, we show how to obtain the
first-order optimal weights when using composite likelihood for inference on a scalar parameter
in the presence of nuisance parameters. These weights depend on the true parameter value and
need to be estimated. Under regularity conditions, the resulting likelihood ratio statistic has the
standard asymptotic null distribution and improved local power. Simulation results inmultivariate
normal models show that estimation of optimal weights maintains the standard approximate
null distribution and produces a visible gain in power with respect to constant weights
Real-time smoothing of car-following data through sensor-fusion techniques
AbstractObservation of vehicles kinematics is an important task for many applications in ITS (Intelligent Transportation Systems). It is at the base of both theoretical analyses and application developments, especially in case of positioning and tracing/tracking of vehicles, car-following analyses and models, navigation and other ATIS (Advanced Traveller Information Systems), ACC (Adaptive Cruise Control) systems, CAS and CWS (Collision Avoidance Systems and Collision Warning Systems) and other ADAS (Advanced Driving Assistance Systems). Modern technologies supply low-cost devices able to collect time series of kinematic and positioning data with medium to very high frequency. Even more data can be (almost continually) collected if vehicle-to-vehicle (V2V) communications come true. However, some of the ITS applications (as well as car-following models, on which many ADAS and ACC are based) require highly accurate measures or, at least, smooth profiles of collected data. Unfortunately, even relatively high-cost devices can collect biased data because of many technical reasons and often this bias could lead to unrealistic kinematics, incorrect absolute positioning and/or inconsistencies between vehicles (e.g. negative spacing). As a consequence, data need filtering in most of the ITS applications. To this aim proper algorithms are required and several sensors and sources of data possibly integrated in order to obtain the maximum quality at the minimal cost. This work addresses the previous issues by developing a specific Kalman smoothing approach. The approach is developed in order to deal with car-following conditions but is conceived to take into account also navigation issues. The performances are analysed with respect to real-world car-following data, voluntarily biased for evaluation purposes. Assessment is carried out with reference to different mixtures of sensors and different sensors accuracies
The influence of platform switching on the biomechanical aspects of the implant-abutment system. A three dimensional finite element study
Objective: To evaluate the biomechanical scenario of platform switching geometric implant-abutment configuration relative to standard configurations by means of finite element analysis.Study Design: A 3D Finite Element Analysis (FEA) was performed on 3 different implant-abutment configurations: a 3.8 mm implant with a matching diameter abutment (Standard Control Design, SCD), a 5.5 mm implant with matching diameter abutment (Wider Control Design, WCD), and a 5.5mm implant with a 3.8 mm abutment (Experimental Design, ED). All the different experimental groups were discretized to over 60000 elements and 100000 nodes, and 130N vertical (axial) and 90N horizontal loads were applied on the coronal portion of the abutment. Von Mises stresses were evaluated and maximum and minimum values were acquired for each implantabutment configuration. Results: The load-induced Von Mises stress (maximum to minumum ranges) on the implant ranged from 150 MPa to 58 Pa (SCD); 45 MPa to 55 Pa (WCD); 190 MPa to 64 Pa (ED). The Von Mises stress on the abutment ranged from 150 MPa to 52 MPa (SCD); 70 MPa to 55 MPa (WCD), and 85 MPa to 42 MPa respectively (ED). The maximum stresses transmitted from the implant-abutment system to the cortical and trabecular bone were 67 Pa and 52 MPa (SCD); 54 Pa and 27 MPa (WCD); 64 Pa and 42 MPa (ED), respectively. When the implant body was evaluated for stresses, a substantial decrease in their levels were observed at the threaded implant region due to the diametral mismatch between implant and abutment for the ED configuration. Conclusion: The platform switching configuration led to not only to a relative decrease in stress levels compared to narrow and wide standard configurations, but also to a notable stress field shift from bone towards the implant system, potentially resulting in lower crestal bone overloading. © Medicina Oral S. L
The influence of platform switching on the biomechanical aspects of the implant-abutment system. A three dimensional finite element study
Objective: To evaluate the biomechanical scenario of platform switching geometric implant-abutment configuration relative to standard configurations by means of finite element analysis.Study Design: A 3D Finite Element Analysis (FEA) was performed on 3 different implant-abutment configurations: a 3.8 mm implant with a matching diameter abutment (Standard Control Design, SCD), a 5.5 mm implant with matching diameter abutment (Wider Control Design, WCD), and a 5.5mm implant with a 3.8 mm abutment (Experimental Design, ED). All the different experimental groups were discretized to over 60000 elements and 100000 nodes, and 130N vertical (axial) and 90N horizontal loads were applied on the coronal portion of the abutment. Von Mises stresses were evaluated and maximum and minimum values were acquired for each implantabutment configuration. Results: The load-induced Von Mises stress (maximum to minumum ranges) on the implant ranged from 150 MPa to 58 Pa (SCD); 45 MPa to 55 Pa (WCD); 190 MPa to 64 Pa (ED). The Von Mises stress on the abutment ranged from 150 MPa to 52 MPa (SCD); 70 MPa to 55 MPa (WCD), and 85 MPa to 42 MPa respectively (ED). The maximum stresses transmitted from the implant-abutment system to the cortical and trabecular bone were 67 Pa and 52 MPa (SCD); 54 Pa and 27 MPa (WCD); 64 Pa and 42 MPa (ED), respectively. When the implant body was evaluated for stresses, a substantial decrease in their levels were observed at the threaded implant region due to the diametral mismatch between implant and abutment for the ED configuration. Conclusion: The platform switching configuration led to not only to a relative decrease in stress levels compared to narrow and wide standard configurations, but also to a notable stress field shift from bone towards the implant system, potentially resulting in lower crestal bone overloading. © Medicina Oral S. L
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