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and time series model selection in small samples","{\"openalex\":\"W1968371014\",\"doi\":\"10.1093/biomet/76.2.297\"}","openalex","2026-05-14T01:13:28.415826245","2026-05-21T02:50:07.193647656","doi","10.1093/biomet/76.2.297","arxivId","openalexId","W1968371014","title","abstractText","A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.","publishedDate","1989-01-01","publishedYear",1989,"venue","Biometrika","citationCount",6343,"authors","keywords",[140,30,141],"pdfUrl","topVenue","Control Systems and Identification","Neural Networks and Applications","paper_W2090330784","Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study","{\"openalex\":\"W2090330784\",\"doi\":\"10.1080/10705510701575396\"}","2026-05-14T00:49:55.419288812","2026-05-21T02:23:26.19725628","10.1080/10705510701575396","W2090330784","Abstract Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered. ACKNOWLEDGMENTS Karen L. Nylund's research was supported by Grant R01 DA11796 from the National Institute on Drug Abuse (NIDA) and Bengt O. Muthén's research was supported by Grant K02 AA 00230 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). We thank Mplus for software support, Jacob Cheadle for programming expertise, and Katherine Masyn for helpful comments. Notes 1In general, the within-class covariance structure can be freed to allow within-class item covariance. a Item probabilities for categorical LCA models are specified by the probability in each cell, and the class means for the continuous LCA are specified by the value in parentheses. 2The number random starts for LCA models with categorical outcomes was specified to be \"starts = 70 7;\" in Mplus. The models with continuous outcomes had differing numbers of random starts. 3It is important to note that when coverage is studied, the random starts option of Mplus should not be used. If it is used, label switching may occur, in that a class for one replication might be represented by another class for another replication, therefore distorting the estimate. 4The models that presented convergence problems were those that were badly misspecified. For example, for the GMM (true k = 3 class model) for n = 500, the convergence rates for the three-, four-, and five-class models were 100%, 87%, and 68%, respectively.","2007-10-23",2007,"Structural Equation Modeling A Multidisciplinary Journal",10712,[155,156,30],"Statistical Methods and Bayesian Inference","Bayesian Methods and Mixture Models","paper_W2156267802","Missing data: Our view of the state of the art.","{\"openalex\":\"W2156267802\",\"doi\":\"10.1037/1082-989x.7.2.147\"}","2026-05-14T00:42:07.293037266","2026-05-21T02:21:58.182464464","10.1037/1082-989x.7.2.147","W2156267802","Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.","2002-01-01",2002,"Psychological Methods",10853,[155,156,30],"paper_W1565746575","Statistical Comparisons of Classifiers over Multiple Data Sets","{\"openalex\":\"W1565746575\"}","2026-05-14T00:41:41.418985774","2026-05-21T02:20:49.181585478","W1565746575","While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.","2006-12-01",2006,11214,[181,182,30],"Machine Learning and Data Classification","Advanced Statistical Methods and Models","paper_W2057765075","Discussion on the paper by Spiegelhalter, Best, Carlin and van der Linde","{\"openalex\":\"W2057765075\",\"doi\":\"10.1111/1467-9868.00353\"}","2026-05-14T00:40:39.239495348","2026-05-21T02:17:52.201456505","10.1111/1467-9868.00353","W2057765075","no abstract","ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/)",12712,[155,156,30],"paper_W4294541781","Regularization Paths for Generalized Linear Models via Coordinate Descent.","{\"openalex\":\"W2097360283\"}","2026-05-14T00:37:52.315286226","2026-05-21T02:15:22.176633835","10.18637/jss.v033.i01","W2097360283","We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.","2010-01-01",2010,"PubMed",14009,[30,207,208],"Sparse and Compressive Sensing Techniques","Face and Expression Recognition","paper_W2122111042","Nearest neighbor pattern classification","{\"openalex\":\"W2122111042\",\"doi\":\"10.1109/tit.1967.1053964\"}","2026-05-14T00:38:19.221864777","2026-05-21T02:13:05.185181461","10.1109/tit.1967.1053964","W2122111042","The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\\ast} \\leq R \\leq R^{\\ast}(2 --MR^{\\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.","1967-01-01",1967,"IEEE Transactions on Information Theory",15894,[30,182,222],"Rough Sets and Fuzzy Logic","paper_W2020999234","Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images","{\"openalex\":\"W2020999234\",\"doi\":\"10.1109/tpami.1984.4767596\"}","2026-05-14T00:32:50.295172785","2026-05-21T02:10:06.181985802","10.1109/tpami.1984.4767596","W2020999234","We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios.","1984-11-01",1984,"IEEE Transactions on Pattern Analysis and Machine Intelligence",17955,[236,207,30],"Image and Signal Denoising Methods","paper_W2049633694","Maximum Likelihood from Incomplete Data Via the EM Algorithm","{\"openalex\":\"W2049633694\",\"doi\":\"10.1111/j.2517-6161.1977.tb01600.x\"}","2026-05-14T00:28:14.306410127","2026-05-21T02:02:13.189655726","10.1111/j.2517-6161.1977.tb01600.x","W2049633694","Summary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.","1977-09-01",1977,"Journal of the Royal Statistical Society Series B (Statistical Methodology)",49526,[140,30,250],"Gaussian Processes and Bayesian Inference","paper_W2135046866","Regression Shrinkage and Selection Via the Lasso","{\"openalex\":\"W2135046866\",\"doi\":\"10.1111/j.2517-6161.1996.tb02080.x\"}","2026-05-14T00:28:03.37298254","2026-05-21T02:01:59.186164628","10.1111/j.2517-6161.1996.tb02080.x","W2135046866","SUMMARY We propose a new method for estimation in linear models. The ‘lasso’ minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.","1996-01-01",1996,51323,[30,156,182],{"_58":264,"_60":265},[],[266],{"_63":267,"_65":268,"_67":269,"_69":270},1780720595145,{"_72":272,"_74":75,"_76":267,"_77":-5,"_78":26,"_79":26,"_80":26,"_81":-5,"_82":-5,"_83":84,"_35":85,"_86":87},[71,271],"[\"portal\",\"industries\"]","industries",[273,274,275,276,277,278,279,280,281,282,283,284],{"_15":345,"_29":346,"_31":-5,"_32":347,"_33":348,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":349,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":346,"_57":348},{"_15":341,"_29":342,"_31":-5,"_32":-5,"_33":343,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":344,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":342,"_57":343},{"_15":335,"_29":336,"_31":-5,"_32":337,"_33":338,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":339,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":336,"_57":338},{"_15":331,"_29":332,"_31":-5,"_32":-5,"_33":333,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":334,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":332,"_57":333},{"_15":327,"_29":328,"_31":-5,"_32":-5,"_33":329,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":330,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":328,"_57":329},{"_15":321,"_29":322,"_31":-5,"_32":323,"_33":324,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":325,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":322,"_57":324},{"_15":316,"_29":317,"_31":-5,"_32":-5,"_33":318,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":319,"_297":26,"_298":320,"_299":26,"_101":-5,"_56":317,"_57":318},{"_15":312,"_29":313,"_31":-5,"_32":-5,"_33":314,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":315,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":313,"_57":314},{"_15":308,"_29":309,"_31":-5,"_32":-5,"_33":310,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":311,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":309,"_57":310},{"_15":304,"_29":305,"_31":-5,"_32":-5,"_33":306,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":307,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":305,"_57":306},{"_15":300,"_29":301,"_31":-5,"_32":-5,"_33":302,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":303,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":301,"_57":302},{"_15":285,"_29":286,"_31":-5,"_32":-5,"_33":287,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":296,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":286,"_57":287},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[326],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[340],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[350,351],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_354":24},"SITE_BASE_URL"]

Statistical Methods and Inference 技术

Statistical Methods and Inference · 相关事件