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91.
Tahani Coolen-Maturi 《统计学通讯:理论与方法》2017,46(19):9476-9493
Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning, and credit scoring. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three-ordered classes or groups. In this article, nonparametric predictive inference (NPI) for three-group ROC analysis for ordinal outcomes is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modeling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. This article also includes results on the volumes under the ROC surfaces and consideration of the choice of decision thresholds for the diagnosis. Two examples are provided to illustrate our method. 相似文献
92.
ABSTRACTThis paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison with the optimal tests for Markov-switching parameters of Carrasco et al. (2014), and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of USA output growth. 相似文献
93.
94.
M-quantile models with application to poverty mapping 总被引:1,自引:0,他引:1
Nikos Tzavidis Nicola Salvati Monica Pratesi Ray Chambers 《Statistical Methods and Applications》2008,17(3):393-411
Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately,
cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation,
using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models
are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random
effects models that include random area effects to account for between area variations. However, such models also depend on
strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow
for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models
was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction
of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do
not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate
for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality.
The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation
of the distribution function of the study variable within the small area of interest both under an M-quantile and a random
effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small
area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the
2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of
poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption
expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are
in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of
inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology
and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile
estimation framework through design-based simulations based on two realistic survey data sets containing small area information
and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function. 相似文献
95.
We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data. 相似文献
96.
《Omega》2024
The demand for glass bottles is exhibiting an upward trend over time. The manufacturing of glass bottles is costlier in terms of time and resources and is associated with a higher level of heat generation and environmental pollution compared to recycling processes. In response to the aforementioned challenges, companies that use glass bottles need to implement strategies to manage their reverse supply chains in conjunction with their traditional supply chains, as the economic and environmental benefits of returned products are unquestionable. Closed-loop supply chains (CLSCs) integrate forward and reverse flows of products and information. This integration helps companies to have a broader view of the whole chain. Despite these advantages, managing CLSCs can be challenging as they are exposed to many uncertainties regarding supply and demand processes, travel times, and quantity/quality of returned products.In this study, we consider the production planning, inventory management, and vehicle routing decisions of a CLSC of beverage glass bottles. We propose an MILP model and rely on a multi-stage adjustable robust optimization (ARO) formulation to deal with the randomness in both the demand for filled bottles and the requests for pickups of empty bottles. We develop an exact oracle-based algorithm to solve the ARO problem and propose a heuristic search algorithm to reduce the solution time. Our numerical experiments not only show the incompetency of the customary method, namely the affine decision rule approach, but also illustrate how our algorithms can solve the small-size problems and significantly improve the quality of the obtained solution for large problems. Furthermore, our numerical results show that robust plans tend to be sparse, meaning the routes are chosen so that empty bottles are transported to production sites in such a way that fewer new bottles need to be ordered. Thus, robust planning makes the CLSCs more environmentally friendly. 相似文献
97.
Searches for faint signals in counting experiments are often encountered in particle physics and astrophysics, as well as in other fields. Many problems can be reduced to the case of a model with independent and Poisson-distributed signal and background. Often several background contributions are present at the same time, possibly correlated. We provide the analytic solution of the statistical inference problem of estimating the signal in the presence of multiple backgrounds, in the framework of objective Bayes statistics. The model can be written in the form of a product of a single Poisson distribution with a multinomial distribution. The first is related to the total number of events, whereas the latter describes the fraction of events coming from each individual source. Correlations among different backgrounds can be included in the inference problem by a suitable choice of the priors. 相似文献
98.
《Journal of Statistical Computation and Simulation》2012,82(5):759-761
In the study of the robust nonparametric regression problem, Oh et al. [The role of pseudo data for robust smoothing with application to wavelet regression, Biometrika 94 (2007), pp. 893–904] developed and named the ES algorithm. In the event that the ES algorithm converges, the robust estimator can be obtained through a sequence of conventional penalized least-squares estimates, the computation of which is fast and straightforward. However, the convergence of the ES algorithm was not established theoretically in Oh et al. In this note, we show that under a certain simple condition, the ES algorithm is monotonic. In particular, the ES algorithm does converge globally in the setting of Oh et al. 相似文献
99.
A. H.M. Rahmatullah Imon 《Journal of applied statistics》2009,36(3):347-358
The heterogeneity of error variance often causes a huge interpretive problem in linear regression analysis. Before taking any remedial measures we first need to detect this problem. A large number of diagnostic plots are now available in the literature for detecting heteroscedasticity of error variances. Among them the ‘residuals’ and ‘fits’ (R–F) plot is very popular and commonly used. In the R–F plot residuals are plotted against the fitted responses, where both these components are obtained using the ordinary least squares (OLS) method. It is now evident that the OLS fits and residuals suffer a huge setback in the presence of unusual observations and hence the R–F plot may not exhibit the real scenario. The deletion residuals based on a data set free from all unusual cases should estimate the true errors in a better way than the OLS residuals. In this paper we propose ‘deletion residuals’ and the ‘deletion fits’ (DR–DF) plot for the detection of the heterogeneity of error variances in a linear regression model to get a more convincing and reliable graphical display. Examples show that this plot locates unusual observations more clearly than the R–F plot. The advantage of using deletion residuals in the detection of heteroscedasticity of error variance is investigated through Monte Carlo simulations under a variety of situations. 相似文献
100.
We consider spatial point processes with a pair correlation function, which depends only on the lag vector between a pair of points. Our interest is in statistical models with a special kind of ‘structured’ anisotropy: the pair correlation function is geometric anisotropic if it is elliptical but not spherical. In particular, we study Cox process models with an elliptical pair correlation function, including shot noise Cox processes and log Gaussian Cox processes, and we develop estimation procedures using summary statistics and Bayesian methods. Our methodology is illustrated on real and synthetic datasets of spatial point patterns. 相似文献