Standard procedures for drawing inferences from complex samples do not apply when the variables of interest z are not observed directly, but must be inferred from secondary random variables x that depend on z stochast...
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Standard procedures for drawing inferences from complex samples do not apply when the variables of interest z are not observed directly, but must be inferred from secondary random variables x that depend on z stochastically. employing Rubin's (1977) approach to missing data in survey research, we present a procedure by which reasonable inferences can be made in such situations. The key is to represent knowledge about latent variables in the form of a predictive distribution, conditional on manifest variables. It is then possible to obtain the expectations of statistics that would have been computed if the values of the latent variables corresponding to sampled units were known, along with variance estimators that account for uncertainty due to both subject sampling and the latency of z.
A novel approach to the analysis of S & P 500 market fluctuations is proposed using a K‐component mixture of regressions model. The Barndorff‐Nielsen and Shephard stochastic model is employed where the estimates...
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A novel approach to the analysis of S & P 500 market fluctuations is proposed using a K‐component mixture of regressions model. The Barndorff‐Nielsen and Shephard stochastic model is employed where the estimates of jumps of log‐returns are governed by Lévy subordinators. Daily VIX and VIX 2 close prices are analyzed as the indicators of log‐return volatility and the corresponding variance of the S & P 500 index using the mixture model. The behavior of the S & P 500 market from 1 August 2005 to 31 December 2009 is analyzed and forecasted. A set of rules are provided to predict monthly fluctuation in the S & P 500 market. The procedure used in this paper gives a novel approach for constructing an “indicator”of non‐Gaussian jump of an empirical data set in finance using mixture of regression (Gaussian) analysis.
The models of standard test theory, having evolved under a trait-oriented psychology, do not reflect the knowledge structures and the problem-solving strategies now seen as central to understanding performance and lea...
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The models of standard test theory, having evolved under a trait-oriented psychology, do not reflect the knowledge structures and the problem-solving strategies now seen as central to understanding performance and learning. In some applications, however, key qualitative distinctions among persons as to structures and strategies can be expressed through mixtures of test theory models, drawing upon substantive theory to delineate the components of the mixture. This approach is illustrated with response latencies to spatial visualization tasks that can be solved by mental rotation or by a nonspatial rule-based strategy. It is assumed that a subject employs the same strategy on all tasks, but the possibility of extending the approach to strategy-switching is discussed.
In this paper, we consider the regime-switching Heston-CIR model, where the parameters of the volatility process are modulated by a Hidden Markov chain and the unobserved regimes. Then, we calibrate the parameters of ...
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In this paper, we consider the regime-switching Heston-CIR model, where the parameters of the volatility process are modulated by a Hidden Markov chain and the unobserved regimes. Then, we calibrate the parameters of the volatility and interest rate processes by the expectation maximization (em) and maximum likelihood estimation (MLE) algorithms, respectively. Next, we use the least square Monte-Carlo (LSM) algorithm to determine the S&P500 American barrier put option under the Heston-CIR model. Finally, by the binomial tree method as a benchmark, we provide some numerical experiments to illustrate the accuracy of the achieved results.
The maximum tolerated dose (MTD) is commonly practiced for dose selection in oncology. Higher doses work as the best effective treatment. Conventionally, the doses are selected in phase 2 from a phase. Perhaps, the qu...
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The maximum tolerated dose (MTD) is commonly practiced for dose selection in oncology. Higher doses work as the best effective treatment. Conventionally, the doses are selected in phase 2 from a phase. Perhaps, the quality of life gets compromised due to the high dose of chemotherapeutic regimes in the early phase of the trial. Alternative chemotherapy administration is Metronomic Chemotherapy (MC). Here very minimal doses are administered to avoid high toxicity. This work is about to handle missing data of circulating endothelial cells (CEC) and supports the optimal biological dose (OBD) for MC. It is performed with mimic data. A data simulation strategy is adopted. Simulation work is performed with R software. It helps to identify the suitable technique to handle missing data. The results conclude that the MC is efficacious by improving the Progression Free Survival (PFS) and Oveall Survival (OS) through controlled toxicity. Now the illustrated example can be extended to explore the impact of CEC for OS. This is a preliminary attempt towards MC to address some critical issues.
Suppose that some components are initially operated in a certain condition and then switched to operating in a different condition. Working hours of the components in condition 1 and condition 2 are respectively obser...
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This paper examines a number of methods of handling missing outcomes in regressive logistic regression modelling of familial binary data, and compares them with an em algorithm approach via a simulation study. The res...
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This paper examines a number of methods of handling missing outcomes in regressive logistic regression modelling of familial binary data, and compares them with an em algorithm approach via a simulation study. The results indicate that a strategy based on imputation of missing values leads to biased estimates, and that a strategy of excluding incomplete families has a substantial effect on the variability of the parameter estimates. Recommendations are made which depend, amongst other factors, on the amount of missing data and on the availability of software.
Genome-wide association study (GWAS) analyses have identified thousands of associations between genetic variants and complex traits. However, it is still a challenge to uncover the mechanisms underlying the associatio...
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Genome-wide association study (GWAS) analyses have identified thousands of associations between genetic variants and complex traits. However, it is still a challenge to uncover the mechanisms underlying the association. With the growing availability of transcriptome data sets, it has become possible to perform statistical analyses targeted at identifying influential genes whose expression levels correlate with the phenotype. Methods such as PrediXcan and transcriptome-wide association study (TWAS) use the transcriptome data set to fit a predictive model for gene expression, with genetic variants as covariates. The gene expression levels for the GWAS data set are then 'imputed' using the prediction model, and the imputed expression levels are tested for their association with the phenotype. These methods fail to account for the uncertainty in the GWAS imputation step, and we propose a collaborative mixed model (CoMM) that addresses this limitation by jointly modelling the multiple analysis steps. We illustrate CoMM's ability to identify relevant genes in the Northern Finland Birth Cohort 1966 data set and extend the model to handle the more widely available GWAS summary statistics.
A multi-locus likelihood of a genetic map is computed based on a mathematical model of chromatid exchange in meiosis that accounts for any type of bivalent configuration in a genetic interval in any specified order of...
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A multi-locus likelihood of a genetic map is computed based on a mathematical model of chromatid exchange in meiosis that accounts for any type of bivalent configuration in a genetic interval in any specified order of genetic markers. The computational problem is to calculate the likelihood (L) and maximize L by choosing an ordering of genetic markers on the map and the recombination distances between markers. This maximum likelihood estimate (MLE) could be found either with a straightforward algorithm or with the proposed recursive linking algorithm that implements the likelihood computation process involving an iterative procedure is called Expectation Maximization (em). The time complexity of the straightforward algorithm is exponential without bound in the number of genetic markers, and implementation of the model with a straightforward algorithm for more than seven genetic markers is not feasible, thus motivating the critical importance of the proposed recursive linking algorithm. The recursive linking algorithm decomposes the pool of genetic markers into segments and renders the model implementable for hundreds of genetic markers. The recursive algorithm is shown to reduce the order of time complexity from exponential to linear in the number of markers. The improvement in time complexity is shown theoretically by a worst-case analysis of the algorithm and supported by run time results using data on linkage group-II of the fungal genome Neurospora crassa.
We introduce a novel probability density function for modeling the distribution of points around an ellipsoidal surface. This density is part of the family of elliptical distributions. We establish the theoretical con...
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We introduce a novel probability density function for modeling the distribution of points around an ellipsoidal surface. This density is part of the family of elliptical distributions. We establish the theoretical convergence properties of the parameter estimators and validate them using simulated data. Furthermore, we propose a mixture model utilizing this density, and we estimate its parameters using the Expectation-Maximization (em) algorithm. To assess its performance, we compare the algorithm to a state-of-the-art ellipse fitting method and conduct experiments on 3D real data obtained from depth cameras.
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