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This paper proposes a new automatic method for spike sorting and tracking non-stationary data based on the Dirichlet Process Mixture (DPM). Data is divided into non-overlapping intervals and mixtures are applied to individual frames rather than to the whole data. In this paper, we have used the information of the previous frame to estimate the cluster parameters of the current interval. Specifically, the means of the clusters in the previous frame are used for estimating the cluster means of the current one, and other parameters are estimated via noninformative priors. The proposed method is capable to track variations in size, shape, or location of clusters as well as detecting the appearance and disappearance of them. We present results in two-dimensional space of first and second principal components (PC1-PC2), but any other feature extraction method leading to the ability of modeling spikes with Normal or t-Student distributions can also be applied. Application of this approach to simulated data and the recordings from anesthetized rat hippocampus confirms its superior performance in comparison to a standard DPM that uses no information from previous frames.

Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.

Background: We investigated the association of dietary patterns and sleep duration by controlling mental health status (MHS) using a joint modeling approach.Methods and materials: The population based cohort study was conducted within the framework of the Isfahan Cohort Study (ICS), including 1383 participants, aged ≥ 35 years were followed from 2007 to 2013. A validated food frequency questionnaire was applied to obtain dietary patterns. Anthropometric measurements, blood pressure, fasting serum lipids and blood sugars were evaluated in both phases of the study. A random effect joint negative binomial and ordinal model were used to estimate diets effect on sleep duration and MHS.Results: Three dietary patterns were identified: Healthy, Iranian and Western dietary patterns. After adjustment for potential confounders, the higher scores of Western diet were associated with sleep hours (OR=1.08 and 95% CI: 1.02‒1.12), the higher scores of the Western diet during the 5 year follow-up was associated with greater sleep duration. Sleep duration and MHS were adversely associated (-0.097 and 95% CI: -0.09‒-0.05)Conclusion: Joint modeling by MHS, sleep duration was positively affected only by western diet. Iranian and healthy diet weren’t associated by sleep hours.

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.

Saliency detection has shown a great role in many image processing applications. This study introduces a new Bayesian framework for saliency detection. In this framework, image saliency is computed as product of three saliencies: location-based, feature-based and centre-surround saliencies. Each of these saliencies is estimated using statistical approaches. The centre-surround saliency is estimated using Dirichlet process mixture model. The authors evaluate their method using five different databases and it is shown that it outperform state-of-the-art methods. Also, they show that the proposed method has a low computational cost.

In this article, the Dirichlet process (DP) is applied to cluster subjects with longitudinal observations. The basis of clustering is the ability of subjects to adapt themselves to new circumstances. Indeed, the basis of clustering depends on the time of changing response variability. This is done by providing a random change-point time in the variance structure of mixed-effects models. The DP is assumed as a prior for the distribution of the random change point. The discrete nature of the DP is utilized to cluster subjects according to the time of adaption. The proposed model is useful to identify groups of subjects with distinctive time-based progressions or declines. Transition mixed-effects models are also used to account for the serial correlation among observations over time. A joint modelling approach is utilized to handle the bias created in these models. The Gibbs sampling technique is adopted to achieve parameter estimates. Performance of the proposed method is evaluated via conducting a simulation study. The usefulness of the proposed model is assessed on a course-evaluation dataset.

In this paper we introduce a new method to the cluster analysis of longitudinal data focusing on the determination of uncertainty levels for cluster memberships. The method uses the Dirichlet-t distribution which notably utilizes the robustness feature of the student-t distribution in the framework of a Bayesian semi-parametric approach together with robust clustering of subjects evaluates the uncertainty level of subjects memberships to their clusters. We let the number of clusters and the uncertainty levels be unknown while fitting Dirichlet process mixture models. Two simulation studies are conducted to demonstrate the proposed methodology. The method is applied to cluster a real data set taken from gene expression studies.

Background: Frequency, severity, and duration of attacks are some major parameters in headache management, affected by some other factors. Ignoring these factors in headache-related studies can lead to incorrect results. We aimed to model both socio-demographic characteristics and headache-associated symptoms related to frequency, severity and duration of headache attacks. Study design: A longitudinal panel study. Methods: Overall, 275 migraines or tension Type Headache (TTH) patients were visited at three different times in 2012 in Isfahan, Iran. On the first visit socio-demographic characteristics and headache symptoms of the patients were asked. In all of the visits, headache frequency, severity and attack duration were recorded. Results: Frequency of headaches was influenced by headache type, age, job status, working hours, residency, disease duration, laterality, and type of pain onset. In terms of intensity, headaches were more severe in patients with migraine-type; those suffering from longer headache history; and those who suffered from vomiting, photophobia, and phonophobia. On the other hand patients with migraine, married people, women and patients suffering from vomiting experienced longer headache attacks. Conclusion: Headache type (migraine/TTH), age, job status, residency, years of headache, laterality, type of onset, nausea, vomiting, photophobia, and phonophobia were the factors to be considered in the studies that would apply frequency, severity, and duration of headache attacks in order to evaluate headache management.

There have been several researches on spike sorting, the process of detection, feature extraction and clustering neural signals generated by brain neurons. How accurate spike sorting is performed, determines how much the results are reliable. Therefore, selecting among several proposed methods for each spike sorting step is a very important task. In this paper, we want to answer this question for the clustering step. We used the most common feature extraction method, i.e. PCA and the first two principal components as features. In order to have fair judgment, 3 datasets with different noise levels were used. We compared some of the most popular methods, selecting between: model-based/non model-based, simple mixtures/Dirichlet process mixtures, Normal/t-distributions for observations, Bayesian/EM clustering. Eventually, some direct and some situation-based conclusions were obtained.

In this paper, we investigate estimation methods to deal with situations where random intercepts are associated to time-varying covariates in the context of linear mixed models. First, a review of previous ways to deal with this so-called endogeneity issue is presented, then a new method based on shared random effects is proposed. Simulation studies and an empirical example are utilized to assess the performance of our proposed method. It is shown that our new approach is more efficient than most competitors and is robust to the misspecification of the random-effects distributions. The authors advise no direct funding is associated with the research reported on this article.

Bed sheet fabric as a kind of home textile has been used since many years ago. Bed sheet is very significant because of being in direct contact with body consecutively for a long period of time. Bed sheet surplus qualitative parameters such as fiber substance, method of printing, finishing, etc., have a significant parameter called handle. In this paper, we proceeded to consider the relationship between fabric handle as a qualitative parameter and physical parameters which influenced the fabric handle using statistical modeling. The statistical model used was ordinal regression model. The modeling was done by SPSS V.19 software. We used 15 bed sheet fabrics. For subjective evaluation of 15 bed sheet fabrics, we selected 55 persons randomly as sample members according to Cochran’s formula. Population was selected from senior BS students and MS students at Isfahan University of Technology (IUT). We asked persons to classify bed sheet fabrics based on their preference of fabric handle from 1 (lowest) to 5 (h...

In this paper, we investigate the impact of time-invariant covariates when fitting transition mixed models. This is carried out by emphasizing on the role of baseline responses on the estimation process. Transition models are allowed for two cases of exogenous and endogenous baseline responses. We illustrate these concepts in the special case of transition linear mixed models with centered time-varying covariates. Results of our simulation studies show that the omission, or the inclusion, of time-invariant covariates is not important in models with exogenous baseline responses, while it has an essential effect on fitting models with the endogenous baseline responses. It is also emphasized that the effect becomes minor when the endogeneity issue is handled. The practical consequences are illustrated in the analysis of a real data set taken from medical sciences.

A common assumption in fitting panel data models is normality of stochastic subject effects. This can be extremely restrictive, making vague most potential features of true distributions. The objective of this article is to propose a modeling strategy, from a semi-parametric Bayesian perspective, to specify a flexible distribution for the random effects in dynamic panel data models. This is addressed here by assuming the Dirichlet process mixture model to introduce Dirichlet process prior for the random-effects distribution. We address the role of initial conditions in dynamic processes, emphasizing on joint modeling of start-up and subsequent responses. We adopt Gibbs sampling techniques to approximate posterior estimates. These important topics are illustrated by a simulation study and also by testing hypothetical models in two empirical contexts drawn from economic studies. We use modified versions of information criteria to compare the fitted models.

In this paper, we consider the analysis of unequally spaced longitudinal data using transition regression models with random effects. Diffusion as well as stabilization processes will be discussed, but our main focus will be on the latter. The initial conditions problem, which usually arises in transition models with random effects, is addressed. The usefulness of the proposed model is assessed on a large database of longitudinal haemoglobin values collected from blood donations by a Dutch private organization.