Information Criterion Approach Research Articles

Assessing the quality of the time delay estimate in acoustic leak localisation

The problem considered in this paper is assessing the quality of the time delay estimate between leak signals measured on water pipes. This is practically important, as a quantitative assessment of the accuracy of time delay estimation (TDE) results makes it possible to infer the reliability of acoustic leak localisation results in a given situation. Three quality assessment approaches are developed by considering the statistical properties of the cross-correlation function (CCF): information-criterion, processing gain, and statistical approaches. In the information-criterion approach, the Bayes factor (BF) is employed to decide the most likely probability distribution of observed CCF peak values. The processing gain approach determines the quality of the time delay estimate using indices that indicate detectability of the CCF peak, namely, the peak-to-side lobe ratio (PSR) and the peak-to-mean ratio (PMR). In the statistical approach, an index termed inconsistency score (ICS) is used to describe the quality of TDE results based on root-mean square of deviations of time delay estimates from their statistical mode. Experimental results show that the proposed approaches provide effective means of assessing the accuracy of the time delay estimate in acoustic leak detection applications. Also, the proposed indices can be employed as figures of merit for selecting best parameters for TDE, for example, filter cut-off frequencies.

Testing Cosmic Acceleration from the Late-Time Universe

We investigate the accelerated cosmic expansion in the late universe and derive constraints on the values of the cosmic key parameters according to different cosmologies such as ΛCDM, wCDM, and w0waCDM. We select 24 baryon acoustic oscillation (BAO) uncorrelated measurements from the latest galaxy surveys measurements in the range of redshift z∈[0.106,2.33] combined with the Pantheon SNeIa dataset, the latest 33 H(z) measurements using the cosmic chronometers (CCs) method, and the recent Hubble constant value measurement measured by Riess 2022 (R22) as an additional prior. In the ΛCDM framework, the model fit yields Ωm=0.268±0.037 and ΩΛ=0.726±0.023. Combining BAO with Pantheon plus the cosmic chronometers datasets we obtain H0=69.76±1.71 km s−1 Mpc−1 and the sound horizon result is rd=145.88±3.32 Mpc. For the flat wCDM model, we obtain w=−1.001±0.040. For the dynamical evolution of the dark energy equation of state, w0waCDM cosmology, we obtain wa=−0.848±0.180. We apply the Akaike information criterion approach to compare the three models, and see that all cannot be ruled out from the latest observational measurements.

Frequentist analysis of basket trials with one-sample Mantel-Haenszel procedures.

Recent substantial advances of molecular targeted oncology drug development is requiring new paradigms for early-phase clinical trial methodologies to enable us to evaluate efficacy of several subtypes simultaneously and efficiently. The concept of the basket trial is getting of much attention to realize this requirement borrowing information across subtypes, which are called baskets. Bayesian approach is a natural approach to this end and indeed the majority of the existing proposals relies on it. On the other hand, it required complicated modeling and may not necessarily control the type 1 error probabilities at the nominal level. In this article, we develop a purely frequentist approach for basket trials based on one-sample Mantel-Haenszel procedure relying on a very simple idea for borrowing information under the common treatment effect assumption over baskets. We show that the proposed Mantel-Haenszel estimator for the treatment effect is consistent under two limiting models of the large strata and sparse data limiting models (dually consistent) and propose dually consistent variance estimators. The proposed estimators are interpretable even if the common treatment effect assumptions are violated. Then, we can design basket trials in a confirmatory matter. We also propose an information criterion approach to identify effective subclasses of baskets.

Development and validation of a nomogram for primary duodenal carcinoma: a multicenter, population-based study.

Aim: This study aimed to develop a predictive model for patients with duodenal carcinoma. Methods: Duodenal carcinoma patients from the Surveillance, Epidemiology, and End Results database (2010-2015) and the First Affiliated Hospital of Nanchang University (2010-2021) were enrolled. A nomogram was constructed according to least absolute shrinkage and selection operator regression analysis, the Akaike information criterion approach and Cox regression analysis. Results: Five independent prognostic factors were significantly associated with the prognosis of the duodenal carcinoma patients. A nomogram was constructed with a C-index in the training and validation cohorts of 0.671 (95% CI: 0.578-0.716) and 0.662 (95% CI: 0.529-0.773), respectively. Conclusion: The established nomogram model provided visualization of the risk of each prognostic factor.

Detecting multiple spatial disease clusters: information criterion and scan statistic approach

BackgroundDetecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specific epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods.MethodsA novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters.ResultsA case study and simulation studies conducted both confirmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity.ConclusionsWe proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches.

Validation of GNSS baseline observation models using information criteria

This paper adapts and discusses the information criterion (IC) approach for the validation of baseline GNSS observation models in the context of outliers. Three IC methods are compared with the conventional procedure of iterative data snooping (IDS). To verify the reliability of the two considered approaches, several scenarios differing in: the magnitude and number of outliers, and the interval between measurement epochs were investigated. Generally, the IDS procedure achieved the highest rates of correct model specification when no outlier existed. However, as the number of outliers was increasing one of the IC methods was starting to achieve the highest rates.

A Jackknife Model Averaging Analysis of RMB Misalignment Estimates

We adopt the Jackknife Model Averaging (JMA) technique to conduct a meta-regression analysis of 925 renminbi (RMB) misalignment estimates generated by 69 studies. The JMA method accounts for model selection and sampling uncertainties, and allows for non-nested model specifications and heteroskedasticity in assessing effects of study characteristics. The RMB misalignment estimates are found to be systematically affected by the choices of data, the theoretical setup and the empirical strategy, in addition to publication attributes of these studies. These study characteristic effects are quite robust to the choice of benchmark study characteristics, to alternative model averaging methods including the heteroskedasticity-robust Mallows approach, the information criterion approach, and the Bayesian model averaging. In evaluating the probabilistic property of RMB misalignment estimates implied by hypothetical composites of study characteristics, we find the evidence of a misaligned RMB, in general, is weak.

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors’ knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a ten-fold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5”) and 48 mm (1.9”), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5”) and 67 mm (2.1”), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with single-sensor.

Selecting between student and normal mixture distributions

ABSTRACTIt is widely recognized that financial data distributions have heavier tails than normal distributions. These tails can be modeled as Student distributions and normal mixture distributions. However, selecting between these distributions has not been previously studied. To this end, this study proposes an information-criterion approach. The efficacy of the proposed method for selecting the number of mixture components is examined via simulation studies that compare the conventional hypothesis test. Empirical results using Japanese stock returns data are also provided.

Variables Selection for Aboveground Biomass Estimations Using Satellite Data: A Comparison between Relative Importance Approach and Stepwise Akaike’s Information Criterion

It is still a major challenge to select appropriate variables from remote sensing sensors, which implicates finding reliable selection methods that can maximize the performance of chosen variables in regression models. In this study, we compare the performance of stepwise variable selection based on Akaike information criterion and an approach that integrates relative importance techniques and the decomposition criteria of R 2 using two different remote sensing data: SPOT-5 and RapidEye images, with the purpose of selecting suitable variables in multiple linear regression models to estimate aboveground biomass. The obtained accuracy of the regression models was evaluated by triple cross-validation. We carried out this study in a mixed pine–oak forest of central Mexico where intensive wood extraction occurs and therefore different levels of degradation are found. We estimated aboveground biomass from field inventory data at the plot level (n = 52) and used well-established allometric equations. The results showed that a better fit was obtained with the explanatory variables selected from the RapidEye image ( R 2 = 0.437 with stepwise variable selection based on the Akaike information criterion approach and R 2 = 0.420 with relative importance techniques) and the approach that integrates the relative importance can generate better regression models to estimate forest biomass with a reduced number of variables and less error in the estimates.

On the prevalence of uninformative parameters in statistical models applying model selection in applied ecology.

Research in applied ecology provides scientific evidence to guide conservation policy and management. Applied ecology is becoming increasingly quantitative and model selection via information criteria has become a common statistical modeling approach. Unfortunately, parameters that contain little to no useful information are commonly presented and interpreted as important in applied ecology. I review the concept of an uninformative parameter in model selection using information criteria and perform a literature review to measure the prevalence of uninformative parameters in model selection studies applying Akaike’s Information Criterion (AIC) in 2014 in four of the top journals in applied ecology (Biological Conservation, Conservation Biology, Ecological Applications, Journal of Applied Ecology). Twenty-one percent of studies I reviewed applied AIC metrics. Many (31.5%) of the studies applying AIC metrics in the four applied ecology journals I reviewed had or were very likely to have uninformative parameters in a model set. In addition, more than 40% of studies reviewed had insufficient information to assess the presence or absence of uninformative parameters in a model set. Given the prevalence of studies likely to have uninformative parameters or with insufficient information to assess parameter status (71.5%), I surmise that much of the policy recommendations based on applied ecology research may not be supported by the data analysis. I provide four warning signals and a decision tree to assist authors, reviewers, and editors to screen for uninformative parameters in studies applying model selection with information criteria. In the end, careful thinking at every step of the scientific process and greater reporting standards are required to detect uninformative parameters in studies adopting an information criteria approach.

A Jackknife Model Averaging Analysis of RMB Misalignment Estimates

We adopt the Jackknife Model Averaging (JMA) technique to conduct a meta-regression analysis of 925 renminbi (RMB) misalignment estimates generated by 69 studies. The JMA method accounts for model selection and sampling uncertainties, and allows for non-nested model specifications and heteroskedasticity in assessing effects of study characteristics. The RMB misalignment estimates are found to be systematically affected by the choices of data, the theoretical setup and the empirical strategy, in addition to publication attributes of these studies. These study characteristic effects are quite robust to the choice of benchmark study characteristics, to alternative model averaging methods including the heteroskedasticity-robust Mallows approach, the information criterion approach, and the Bayesian model averaging. In evaluating the probabilistic property of RMB misalignment estimates implied by hypothetical composites of study characteristics, we find the evidence of a misaligned RMB, in general, is weak.

Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models.

The spatial scan statistic is commonly used to detect spatial and/or temporal disease clusters in epidemiological studies. Although multiple clusters in the study space can be thus identified, current theoretical developments are mainly based on detecting a ‘single’ cluster. The standard scan statistic procedure enables the detection of multiple clusters, recursively identifying additional ‘secondary’ clusters. However, their p-values are calculated one at a time, as if each cluster is a primary one. Therefore, a new procedure that can accurately evaluate multiple clusters as a whole is needed. The present study focuses on purely temporal cases and then proposes a new test procedure that evaluates the p-value for multiple clusters, combining generalized linear models with an information criterion approach. This framework encompasses the conventional, currently widely used detection procedure as a special case. An application study adopting the new framework is presented, analysing the Japanese daily incidence of out-of-hospital cardiac arrest cases. The analysis reveals that the number of the incident increases around New Year’s Day in Japan. Further, simulation studies undertaken confirm that the proposed method possesses a consistency property that tends to select the correct number of clusters when the truth is known.

PHYLOGENETIC DIVERSITY AS A KEY TO UNDERSTAND MECHANISMS OF NEW WORLD MARSUPIALS DIVERSIFICATION (DIDELPHIMORPHIA: DIDELPHIDAE)

Phylogenetic diversity is a measure that can provide information about the history of diversification of a lineage and the events that shaped contemporary communities, as macroevolutionary processes leave marks on the relationships of the lineages. However, this approach has been applied primarily in studies on local scales, and rarely has been employed to analyze regional or continental patterns. Here we mapped spatial patterns that could identify regions of intense diversification or dispersion of the New World marsupials (order Didelphimorphia) by analyzing the spatial variation of species richness and phylogenetic diversity (PD). Based on the phylogeny and occurrences of the Didelphimorphia species we calculated three diversity measures, and applied a model selection by information criteria approach to identify the relative importance of four environmental determinants: potential evapotranspiration, productivity, topography, and the number of ecoregions. Species richness and PD are highly correlated, and both are associated to productivity, topography, and number of ecoregions, while relative PD (rPD) is associated to potential evapotranspiration and topography. Productivity showed a clear positive relation with species richness and PD, consistent with the productive energy, but rPD was affected by potential evapotranspiration, which points to a role of environmental energy in the process of diversification. This study is pioneer in testing and confirming effects of the productive and environmental energy on species diversification as other studies analyzing the same questions neither could isolate the effects of the two energy measurements due to their collinearity, nor did make any distinction between energy hypotheses. Physical geography and ecological differences among habitats can shape biogeographical patterns and drive speciation in continental faunas, leaving marks on their phylogenetic register. Comparing patterns among other autochthonous taxa whose evolutionary history and geographic range is congruent to the Didelphimorphia could provide interesting clues about the processes that shaped the diversification of Neotropical fauna.

MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for wide data observation. In remote sensing, machine learning method could be used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.

Estimation of Damage Location of Rock Based on Denoised Acoustic Emission Signals Using Wavelet Packet Algorithm

Abstract The damage source location of acoustic emission (AE) signals is studied in this investigation. Noises can influence the accuracy of calculation significantly as the compressive wave velocity in rock is very fast. A novel fractional power model is proposed for use in wavelet packet transform analysis to decompose, filter, and reconstruct AE signals. Analysis of uniaxial compression testing data on sandstone samples has revealed that the denoising scheme can result in a higher signal to noise ratio (SNR) and a lower root mean square error (RMSE) compared with the conventional soft and hard thresholding functions. The arrival time of AE signals needs to be determined to perform the time difference of arrival (TDOA) analysis. A combined maximum likelihood estimation and Akaike’s information criterion (MLE–AIC) approach is initially used to compute the arrival time. To locate the damage source, a combined least squares–particle swarm optimization (LSPSO) method is originally proposed, which can reduce the number of iterations compared to the conventional particle swarm optimization (PSO) analysis. Two techniques have been evaluated based on either a predefined wave velocity range or interval theory to improve the accuracy of damage source location. Simulated artificial signals and AE signals obtained from pencil-lead breakage tests and uniaxial compression tests are analyzed using the LSPSO algorithm. The comparison of located damage source obtained from the current approach and the PCI-2 AE system has demonstrated the efficacy of the proposed analysis scheme.

Extended models for nosocomial infection: parameter estimation and model selection.

We consider extensions to previous models for patient level nosocomial infection in several ways, provide a specification of the likelihoods for these new models, specify new update steps required for stochastic integration, and provide programs that implement these methods to obtain parameter estimates and model choice statistics. Previous susceptible-infected models are extended to allow for a latent period between initial exposure to the pathogen and the patient becoming themselves infectious, and the possibility of decolonization. We allow for multiple facilities, such as acute care hospitals or long-term care facilities and nursing homes, and for multiple units or wards within a facility. Patient transfers between units and facilities are tracked and accounted for in the models so that direct importation of a colonized individual from one facility or unit to another might be inferred. We allow for constant transmission rates, rates that depend on the number of colonized individuals in a unit or facility, or rates that depend on the proportion of colonized individuals. Statistical analysis is done in a Bayesian framework using Markov chain Monte Carlo methods to obtain a sample of parameter values from their joint posterior distribution. Cross validation, deviance information criterion and widely applicable information criterion approaches to model choice fit very naturally into this framework and we have implemented all three. We illustrate our methods by considering model selection issues and parameter estimation for data on methicilin-resistant Staphylococcus aureus surveillance tests over 1 year at a Veterans Administration hospital comprising seven wards.

Environmental Factors Associated with the Upstream Migration of Fall‐Run Chinook Salmon in a Regulated River

AbstractWe examined upstream migration patterns of adult Chinook Salmon Oncorhynchus tshawytscha in relation to environmental factors and two management actions (installation of a rock barrier at a distributary and managed pulse flows). Data was collected using a portable resistance board weir and a Vaki Riverwatcher system that provided accurate daily counts of fall‐run Chinook Salmon on their spawning migration. Akaike's information criterion and multimodel inferential approaches, as well as generalized additive models, were used to assess the relative influence of water temperature, flow, moon illumination, weather, operation of a rock barrier, and managed pulse flows to explain the magnitude of daily counts and proportions of Chinook Salmon observed at the weir. Over the 12‐year study period (2003–2014), we observed 38,206 Chinook Salmon. The installation of a rock barrier in the lower reaches of the San Joaquin River had positive and consistent influences on daily counts in the years it was installed. Although managed pulse flows to stimulate upstream migration have been used since the early 1990s, our analyses found managed pulse flows only appeared in the top generalized linear models in 2 of the 11 complete years of data analyzed. Managed pulse flows resulted in immediate increases in daily passages, but the response was brief and represented a small portion of the total run. A strong nonlinear response between migratory activity and discharge levels was observed for Chinook Salmon, indicating no additional increase in daily counts when pulse flows exceeded 20 m3/s. Current management requirements in the Stanislaus River exceed this level and adjustment should be considered based on the findings of this study, particularly given the need to balance beneficial uses of a limited water supply. This study provides a scientific approach to determine biologically relevant flow prescriptions for upstream migration of fish in regulated streams.Received February 5, 2016; accepted September 15, 2016Published online December 21, 2016

Climwin: An R Toolbox for Climate Window Analysis.

When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.

Model selection in spectroscopic ellipsometry data analysis: Combining an information criteria approach with screening sensitivity analysis

In the field of optical metrology, the selection of the best model to fit experimental data is absolutely nontrivial problem. In practice, this is a very subjective and formidable task which highly depends on metrology expert opinion. In this paper, we propose a systematic approach to model selection in ellipsometric data analysis. We apply two well-established statistical methods for model selection, namely, the Akaike (AIC) and Bayesian (BIC) Information Criteria, to compare different dispersion models with various complexities and objectively determine the “best” one from a set of candidate models. The information criteria suggest the most optimal way to quantify the balance between goodness of fit and model complexity. In combination with screening-type parametric sensitivity analysis based on so-called “elementary effects” (the Morris method) this approach allows to compare and rate various models, identify key model parameters and significantly enhance process of ellipsometric measurements evaluation.