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Decision Theory and Bayesian Analysis","{\"openalex\":\"W1988520084\",\"doi\":\"10.1007/978-1-4757-4286-2\"}","openalex","2026-05-14T01:01:40.206972151","2026-05-21T02:38:08.183981758","doi","10.1007/978-1-4757-4286-2","arxivId","openalexId","W1988520084","title","abstractText","publishedDate","1985-01-01","publishedYear",1985,"venue","Springer series in statistics","citationCount",7786,"authors","keywords",[141,142,30],"pdfUrl","https://link.springer.com/content/pdf/bfm%3A978-1-4757-4286-2%2F1","topVenue","Statistical Methods and Bayesian Inference","Bayesian Methods and Mixture Models","paper_W1984524168","Microeconometrics","{\"openalex\":\"W1984524168\",\"doi\":\"10.1017/cbo9780511811241\"}","2026-05-14T01:01:10.289942063","2026-05-21T02:36:12.208168315","10.1017/cbo9780511811241","W1984524168","This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.","2005-05-09",2005,"Cambridge University Press eBooks",8074,[30,156,157],"Statistical Methods and Applications","Data Analysis with R","paper_W2056795330","Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences","{\"openalex\":\"W2056795330\",\"doi\":\"10.1046/j.1467-9884.2003.t01-2-00383_4.x\"}","2026-05-14T00:51:22.289206542","2026-05-21T02:27:22.610284523","10.1046/j.1467-9884.2003.t01-2-00383_4.x","W2056795330","2003-11-21",2003,"Journal of the Royal Statistical Society Series D (The Statistician)",9374,[30,170,142],"Sensory Analysis and Statistical Methods","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,[182,30,183],"Machine Learning and Data Classification","Statistical Methods and Inference","paper_W2089468765","Principal component analysis","{\"openalex\":\"W2089468765\",\"doi\":\"10.1016/0169-7439(87)80084-9\"}","2026-05-14T00:41:35.262370781","2026-05-21T02:20:15.187345559","10.1016/0169-7439(87)80084-9","W2089468765","1987-08-01",1987,"Chemometrics and Intelligent Laboratory Systems",11476,[196,197,30],"Spectroscopy and Chemometric Analyses","Computational Drug Discovery Methods","paper_W4241653265","Regression Quantiles","{\"openalex\":\"W4241653265\",\"doi\":\"10.2307/1913643\"}","2026-05-14T00:40:44.30469687","2026-05-21T02:18:08.255190881","10.2307/1913643","W4241653265","Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at","1978-01-01",1978,"Econometrica",12648,[30,211,157],"Advanced Statistical Modeling Techniques","paper_W260994251","An R Companion to Applied Regression","{\"openalex\":\"W260994251\"}","2026-05-14T00:38:29.357414847","2026-05-21T02:13:33.18618554","W260994251","Preface 1. Getting Started With R 2. Reading and Manipulating Data 3. Exploring and Transforming Data 4. Fitting Linear Models 5. Fitting Generalized Linear Models 6. Diagnosing Problems in Linear and Generalized Linear Models 7. Drawing Graphs 8. Writing Programs References Author Index Subject Index Command Index Data Set Index Package Index About the Authors","2010-11-29",2010,15431,[157,30],"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,[183,30,236],"Rough Sets and Fuzzy Logic","paper_W2774486220","lmerTest Package: Tests in Linear Mixed Effects Models","{\"openalex\":\"W2774486220\",\"doi\":\"10.18637/jss.v082.i13\"}","2026-05-14T00:30:30.282038599","2026-05-21T02:06:39.190253174","10.18637/jss.v082.i13","W2774486220","One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.","2017-01-01",2017,"Journal of Statistical Software",22905,[141,30,157],"https://www.jstatsoft.org/index.php/jss/article/view/v082i13/v82i13.pdf",true,"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,"Journal of the Royal Statistical Society Series B (Statistical Methodology)",51323,[183,142,30],{"_58":266,"_60":267},[],[268],{"_63":269,"_65":270,"_67":271,"_69":272},1780608563260,{"_72":274,"_74":75,"_76":269,"_78":-5,"_79":26,"_80":26,"_81":26,"_82":-5,"_83":-5,"_84":85,"_35":86,"_87":88},[71,273],"[\"portal\",\"industries\"]","industries",[275,276,277,278,279,280,281,282,283,284,285,286],{"_15":346,"_29":347,"_31":-5,"_32":348,"_33":349,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":350,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":347,"_57":349},{"_15":342,"_29":343,"_31":-5,"_32":-5,"_33":344,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":345,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":343,"_57":344},{"_15":336,"_29":337,"_31":-5,"_32":338,"_33":339,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":340,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":337,"_57":339},{"_15":332,"_29":333,"_31":-5,"_32":-5,"_33":334,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":335,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":333,"_57":334},{"_15":328,"_29":329,"_31":-5,"_32":-5,"_33":330,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":331,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":329,"_57":330},{"_15":322,"_29":323,"_31":-5,"_32":324,"_33":325,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":326,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":323,"_57":325},{"_15":318,"_29":319,"_31":-5,"_32":-5,"_33":320,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":321,"_299":26,"_300":251,"_301":26,"_18":-5,"_56":319,"_57":320},{"_15":314,"_29":315,"_31":-5,"_32":-5,"_33":316,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":317,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":315,"_57":316},{"_15":310,"_29":311,"_31":-5,"_32":-5,"_33":312,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":313,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":311,"_57":312},{"_15":306,"_29":307,"_31":-5,"_32":-5,"_33":308,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":309,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":307,"_57":308},{"_15":302,"_29":303,"_31":-5,"_32":-5,"_33":304,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":305,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":303,"_57":304},{"_15":287,"_29":288,"_31":-5,"_32":-5,"_33":289,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":290,"_42":-5,"_44":-5,"_291":292,"_293":-5,"_294":75,"_295":296,"_297":298,"_299":26,"_300":85,"_301":26,"_18":-5,"_56":288,"_57":289},"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","tax_ind_blockchain","区块链",[327],"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",[341],"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",[351,352],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_355":24},"SITE_BASE_URL"]

Advanced Statistical Methods and Models 技术

Advanced Statistical Methods and Models · 衍生技术