Journal articles: 'Multivariate Ratio'

1

Eisenberg, Bennett. "The multivariate Gini ratio." Statistics & Probability Letters 96 (January 2015): 292–98. http://dx.doi.org/10.1016/j.spl.2014.10.009.

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2

Marchese, Scott, and Guoqing Diao. "Density ratio model for multivariate outcomes." Journal of Multivariate Analysis 154 (February 2017): 249–61. http://dx.doi.org/10.1016/j.jmva.2016.11.008.

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3

Kim, Ilmun, and Sangun Park. "Likelihood ratio tests for multivariate normality." Communications in Statistics - Theory and Methods 47, no. 8 (September 27, 2017): 1923–34. http://dx.doi.org/10.1080/03610926.2017.1332218.

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4

Hui, Siu L., and Saul H. Rosenberg. "Multivariate Slope Ratio Assay with Repeated Measurements." Biometrics 41, no. 1 (March 1985): 11. http://dx.doi.org/10.2307/2530638.

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5

O’ Brien, Peter C. "A multivariate generalization of von neumann's ratio." Communications in Statistics - Theory and Methods 23, no. 1 (January 1994): 239–47. http://dx.doi.org/10.1080/03610929408831250.

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6

Heagerty, Patrick J., and Scott L. Zeger. "Multivariate Continuation Ratio Models: Connections and Caveats." Biometrics 56, no. 3 (September 2000): 719–32. http://dx.doi.org/10.1111/j.0006-341x.2000.00719.x.

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7

Schneeberger, Hans, and Karlheinz Fleischer. "The Multivariate Ratio Estimation/Die mehrdimensionale Verhältnisschätzung." Jahrbücher für Nationalökonomie und Statistik 211, no. 5-6 (March 1, 1993): 524–38. http://dx.doi.org/10.1515/jbnst-1993-5-614.

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8

Lim, Johan, Erning Li, and Shin-Jae Lee. "Likelihood ratio tests of correlated multivariate samples." Journal of Multivariate Analysis 101, no. 3 (March 2010): 541–54. http://dx.doi.org/10.1016/j.jmva.2009.10.011.

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9

de Carvalho, Miguel, and Anthony C. Davison. "Spectral Density Ratio Models for Multivariate Extremes." Journal of the American Statistical Association 109, no. 506 (April 3, 2014): 764–76. http://dx.doi.org/10.1080/01621459.2013.872651.

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10

Wang, Yashi, and Peng Zhao. "Multivariate likelihood ratio ordering of conditional order statistics." Journal of Systems Science and Complexity 23, no. 6 (December 2010): 1143–52. http://dx.doi.org/10.1007/s11424-010-7269-8.

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11

Wu, Jianhong, and Lixing Zhu. "Empirical likelihood ratio tests for multivariate regression models." Frontiers of Mathematics in China 2, no. 1 (March 2007): 149–68. http://dx.doi.org/10.1007/s11464-007-0011-8.

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12

Park, Hyo-Il. "A Note on the Likelihood Ratio Simultaneous Multivariate Tests." Korean Data Analysis Society 20, no. 3 (June 30, 2018): 1089–97. http://dx.doi.org/10.37727/jkdas.2018.20.3.1089.

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13

Hermy, Martin, and Paul J. Lewi. "Multivariate Ratio Analysis: A Graphical Method for Ecological Ordination." Ecology 72, no. 2 (April 1991): 735–39. http://dx.doi.org/10.2307/2937213.

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14

Belzunce, Félix, Selma Gurler, and José M. Ruiz. "REVISITING MULTIVARIATE LIKELIHOOD RATIO ORDERING RESULTS FOR ORDER STATISTICS." Probability in the Engineering and Informational Sciences 25, no. 3 (May 17, 2011): 355–68. http://dx.doi.org/10.1017/s0269964811000052.

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In this article, we establish some results concerning the likelihood ratio order of random vectors of order statistics in the case of independent but not necessarily identically distributed observations and for the case of possible dependent observations. Applications of these results to provide comparisons of conditional order statistics are also given.

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15

Scheike, T. H., and Y. Sun. "On cross-odds ratio for multivariate competing risks data." Biostatistics 13, no. 4 (June 12, 2012): 680–94. http://dx.doi.org/10.1093/biostatistics/kxs017.

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16

Juraska, Michal, and Peter B. Gilbert. "Mark-specific hazard ratio model with missing multivariate marks." Lifetime Data Analysis 22, no. 4 (October 28, 2015): 606–25. http://dx.doi.org/10.1007/s10985-015-9353-9.

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17

GADRE, M. P., and R. N. RATTIHALLI. "LIKELIHOOD RATIO BASED MULTI-ATTRIBUTE CONTROL CHART." International Journal of Reliability, Quality and Safety Engineering 12, no. 02 (April 2005): 149–66. http://dx.doi.org/10.1142/s0218539305001744.

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In a production process, when quality of the product depends on more than one characteristic, 'Multivariate Quality Control' (MQC) techniques are efficiently used. Many MQC techniques have been developed to control the multivariate variable processes, but no much work has been reported to control the multivariate attribute processes. In this article, to detect a change in the vector of fraction non-conforming, we develop 'Likelihood Ratio based Multi-Attribute Control Chart' (LR-MACC) using the exact joint distribution and the LR-test under multinomial setup. It is verified that, in some situations, LR-MACC is superior to the MNP chart proposed by Lu et al.7 When MACC gives a signal, the attributes responsible are not readily identifiable. Therefore, a procedure to detect the responsible attributes is also developed.

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18

Cong, Lin, and Weixin Yao. "A Likelihood Ratio Test of a Homoscedastic Multivariate Normal Mixture Against a Heteroscedastic Multivariate Normal Mixture." Econometrics and Statistics 18 (April 2021): 79–88. http://dx.doi.org/10.1016/j.ecosta.2021.01.002.

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19

Provost, Serge B. "Likelihood ratio test for independence with partial multivariate normal data." Communications in Statistics - Theory and Methods 17, no. 6 (January 1988): 1763–75. http://dx.doi.org/10.1080/03610928808829712.

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20

Lien, Donald, and Keshab Shrestha. "Estimating optimal hedge ratio: a multivariate skew-normal distribution approach." Applied Financial Economics 20, no. 8 (April 2010): 627–36. http://dx.doi.org/10.1080/09603100903459907.

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21

Iyengar, Satish. "On a Lower Bound for the Multivariate Normal Mills' Ratio." Annals of Probability 14, no. 4 (October 1986): 1399–403. http://dx.doi.org/10.1214/aop/1176992380.

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22

BROOKS, R., J. P. SHELLY, J. FAN, L. ZHAI, and D. K. P. CHAU. "Much more than a ratio: multivariate selection on female bodies." Journal of Evolutionary Biology 23, no. 10 (September 6, 2010): 2238–48. http://dx.doi.org/10.1111/j.1420-9101.2010.02088.x.

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23

Zadora, G., T. Neocleous, and C. G. G. Aitken. "Recent developments in likelihood ratio models for multivariate compositional data." Science & Justice 50, no. 1 (March 2010): 30. http://dx.doi.org/10.1016/j.scijus.2009.11.023.

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24

Hord, K., R. Dolan, Y. Huang, A. Knezevic, and D. Gupta. "Immunosuppression and Cardiac Allograft Vasculopathy: A Multivariate Odds Ratio Analysis." Journal of Heart and Lung Transplantation 35, no. 4 (April 2016): S200—S201. http://dx.doi.org/10.1016/j.healun.2016.01.561.

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25

Padmanabhan, A. R. "Nonparametric Estimation of a Ratio of Multivariate Means and Applications." Applied Statistics 40, no. 3 (1991): 443. http://dx.doi.org/10.2307/2347524.

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26

Chen, Xiaohong, and Yanqin Fan. "Pseudo-likelihood ratio tests for semiparametric multivariate copula model selection." Canadian Journal of Statistics 33, no. 3 (September 2005): 389–414. http://dx.doi.org/10.1002/cjs.5540330306.

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27

Hürlimann, Werner. "Multivariate Likelihood Ratio Order for Skew-Symmetric Distributions with a Common Kernel." ISRN Probability and Statistics 2013 (November 28, 2013): 1–4. http://dx.doi.org/10.1155/2013/614938.

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The multivariate likelihood ratio order comparison of skew-symmetric distributions with a common kernel is considered. Two multivariate likelihood ratio perturbation invariance properties are derived.

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28

Cai, Ming, Zhong Han, Xia He, and JinFei Zhang. "Usefulness of International Normalized Ratio to Albumin Ratio for Evaluation of Mortality in Hepatitis B Virus-Associated Decompensated Cirrhosis." BioMed Research International 2021 (May 11, 2021): 1–6. http://dx.doi.org/10.1155/2021/6664574.

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Background. We sought to determine the prognostic value of prothrombin time-international normalized ratio to albumin ratio (PTAR) in patients with hepatitis B virus-associated decompensated cirrhosis (HBV-DeCi). Methods. The study enrolled 166 HBV-DeCi patients. Multivariate regression analysis was performed to identify predictors associated with mortality. Results. Among the 166 HBV-DeCi patients, 27 (16.3%) had died by 30 days after admission. PTAR was markedly increased in nonsurvivors compared with survivors, and had a significant positive correlation with disease severity. Multivariate analysis identified PTAR as an effective independent predictor for mortality in HBV-DeCi patients. Conclusions. High PTAR was associated with poor outcomes and can act as a novel prognostic predictor for mortality in HBV-DeCi patients.

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29

Saruhashi, Takumi, Masato Ohkubo, and Yasushi Nagata. "Study on Likelihood-Ratio-Based Multivariate EWMA Control Chart Using Lasso." Quality Innovation Prosperity 25, no. 1 (March 31, 2021): 3–15. http://dx.doi.org/10.12776/qip.v25i1.1552.

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Purpose: When applying exponentially weighted moving average (EWMA) multivariate control charts to multivariate statistical process control, in many cases, only some elements of the controlled parameters change. In such situations, control charts applying Lasso are useful. This study proposes a novel multivariate control chart that assumes that only a few elements of the controlled parameters change. Methodology/Approach: We applied Lasso to the conventional likelihood ratio-based EWMA chart; specifically, we considered a multivariate control chart based on a log-likelihood ratio with sparse estimators of the mean vector and variance-covariance matrix. Findings: The results show that 1) it is possible to identify which elements have changed by confirming each sparse estimated parameter, and 2) the proposed procedure outperforms the conventional likelihood ratio-based EWMA chart regardless of the number of parameter elements that change. Research Limitation/Implication: We perform sparse estimation under the assumption that the regularization parameters are known. However, the regularization parameters are often unknown in real life; therefore, it is necessary to discuss how to determine them. Originality/Value of paper: The study provides a natural extension of the conventional likelihood ratio-based EWMA chart to improve interpretability and detection accuracy. Our procedure is expected to solve challenges created by changes in a few elements of the population mean vector and population variance-covariance matrix.

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30

Zhang, Xinrui, Feng Yu, Zheng Zhao, Junhao Mai, Yibo Zhou, Guojie Tan, and Xuekui Liu. "The Prognostic Significance of Lymph Node Ratio and Log Odds Ratio in Laryngeal Squamous Cell Carcinoma." OTO Open 2, no. 3 (July 2018): 2473974X1879200. http://dx.doi.org/10.1177/2473974x18792008.

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Objective To investigate whether lymph node ratio (LNR) and log odds ratio (LODDS) have prognostic significance for overall survival (OS) and disease-free survival (DFS) in patients with laryngeal squamous cell cancer (LSCC) treated with curative intent. Study Design Case-control study. Setting University hospital. Subjects and Methods Records of 229 patients with LSCC who underwent surgery with a curative intent with or without adjuvant treatment from 2000 to 2014 were reviewed. The clinicopathological parameters LNR and LODDS were analyzed; univariate and multivariate analysis was performed to evaluate the prognosis of each for OS and DFS. Results The 5-year OS was 81.7% for LNR ≤0.233 and 47.1% for LNR >0.233, and the 5-year OS was 79.6% for LODDS ≤–0.1 and 51.8% for LODDS >–0.1, respectively. In the univariate analysis, the independent variables were subsites, pT stage (pT1 and pT2 vs pT3 and pT4), pN, pTNM, alcohol consumption, and LNR and LODDS ( P < .05). By multivariate analysis, we determined that subsites, pT stage, alcohol consumption, LNR, and LODDS were independent prognostic predictors of survival ( P < .05). Univariate and multivariate models identified that both LNR and LODDS were significant prognostic factors for survival. However, the hazard ratio (HR) for LNR >0.233 vs ≤0.233 was 8.95 (95% confidence interval [CI], 3.18-25.16; P < .001) in OS, and the HR was 11.37 (95% CI, 4.02-32.15; P < .001) in DFS. The risk of LNR was noticeably greater than other factors. Conclusions LNR and LODDS were both prognostic factors for OS and DFS. However, LNR was confirmed as a more reliable indicator for evaluating the prognosis, and it can be used to increase the prognostic value of the traditional TNM classification of LSCC.

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31

Yazdi, Abbas Javaherian, Terje Haukaas, Tony Yang, and Paolo Gardoni. "Multivariate Fragility Models for Earthquake Engineering." Earthquake Spectra 32, no. 1 (February 2016): 441–61. http://dx.doi.org/10.1193/061314eqs085m.

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This paper employs a logistic regression technique to develop multivariate damage models. The models are intended for performance assessments that require the probability that structural components are in one of several damage states. As such, the developments represent an extension of the univariate fragility functions that are omnipresent in contemporary performance-based earthquake engineering. The multivariate logistic regression models that are put forward here eliminate several of the limitations of univariate fragility functions. Furthermore, the new models are readily substituted for existing fragility functions without any modifications to the existing performance-based analysis methodologies. To demonstrate the proposed modeling approach, a large number of tests of reinforced concrete shear walls are employed to develop multivariate damage models. It is observed that the drift ratio and aspect ratio of concrete shear walls are among the parameters that are most influential on the damage probabilities.

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32

Zhang, Xiao Ping, Chang Zheng Sun, and Tong Feng Zhao. "Research on Multivariate Composite Super early Strength Grouting Materials." Applied Mechanics and Materials 477-478 (December 2013): 990–95. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.990.

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Super early strength high performance grouting material was produced using high alumina cement, ordinary portland cement, gypsum and silica fume. The different glue sand ratio and the cement sand ratio 1.0 mix microstructure were studied systematically. The results showed that the system with a variety of admixture using hingh mortar ratio can be prepared for the initial flow ratio greater than 325{mm},, 30min flow greater than 280{mm}, 2h compressive strength 34.80{MPa}, 24h bending over 13.82{MPa}, 28d compressive strength greater than 99.90{MPa}, 28d compressive strength greater than 56d compressive strength. The system of early SEM microstructures showed good crystal growth, structure compact.

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33

Ngesa, Oscar O., G. O. Orwa, R. O. Otieno, and H. M. Murray. "Multivariate Ratio Estimator of the Population Total under Stratified Random Sampling." Open Journal of Statistics 02, no. 03 (2012): 300–304. http://dx.doi.org/10.4236/ojs.2012.23036.

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34

Srivastava, M. S., and K. J. Worsley. "Likelihood Ratio Tests for a Change in the Multivariate Normal Mean." Journal of the American Statistical Association 81, no. 393 (March 1986): 199–204. http://dx.doi.org/10.1080/01621459.1986.10478260.

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35

Singh, Housila P., Sunil Kumar, and Sandeep Bhougal. "Multivariate Ratio Estimation in Presence of Non-Response in Successive Sampling." Journal of Statistical Theory and Practice 5, no. 4 (December 2011): 591–611. http://dx.doi.org/10.1080/15598608.2011.10483733.

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36

Wang, Yining, and Gang Li. "A dose finding procedure based on the multivariate likelihood ratio test." Journal of Statistical Planning and Inference 104, no. 2 (June 2002): 417–25. http://dx.doi.org/10.1016/s0378-3758(01)00244-0.

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37

Cebrián, A. Arcos, and M. Rueda García. "Variance estimation using auxiliary information: An almost unbiased multivariate ratio estimator." Metrika 45, no. 1 (January 1997): 171–78. http://dx.doi.org/10.1007/bf02717100.

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38

Liu, Wei-Han. "Optimal hedge ratio estimation and hedge effectiveness with multivariate skew distributions." Applied Economics 46, no. 12 (February 11, 2014): 1420–35. http://dx.doi.org/10.1080/00036846.2013.875112.

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39

Chen, Huaihou, and Taizhong Hu. "Multivariate likelihood ratio orderings between spacings of heterogeneous exponential random variables." Metrika 68, no. 1 (June 27, 2007): 17–29. http://dx.doi.org/10.1007/s00184-007-0140-9.

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40

García, M. Rueda, A. Arcos Cebrián, and E. Artés Rodríguez. "Quantile interval estimation in finite population using a multivariate ratio estimator." Metrika 47, no. 1 (January 1998): 203–13. http://dx.doi.org/10.1007/bf02742873.

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41

Catana, Luigi-Ionut, and Vasile Preda. "A New Stochastic Order of Multivariate Distributions: Application in the Study of Reliability of Bridges Affected by Earthquakes." Mathematics 11, no. 1 (December 26, 2022): 102. http://dx.doi.org/10.3390/math11010102.

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In this article, we introduce and study a new stochastic order of multivariate distributions, namely, the conditional likelihood ratio order. The proposed order and other stochastic orders are analyzed in the case of a bivariate exponential distributions family. The theoretical results obtained are applied for studying the reliability of bridges affected by earthquakes. The conditional likelihood ratio order involves the multivariate stochastic ordering; it resembles the likelihood ratio order in the univariate case but is much easier to verify than the likelihood ratio order in the multivariate case. Additionally, the likelihood ratio order in the multivariate case implies this ordering. However, the conditional likelihood ratio order does not imply the weak hard rate order, and it is not an order relation on the multivariate distributions set. The new conditional likelihood ratio order, together with the likelihood ratio order and the weak hazard rate order, were studied in the case of the bivariate Marshall–Olkin exponential distributions family, which has a lack of memory type property. At the end of the paper, we also presented an application of the analyzed orderings for this bivariate distributions family to the study of the effects of earthquakes on bridges.

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42

Lee, James, Chuen Seng Tan, and Kee Seng Chia. "A Practical Guide for Multivariate Analysis of Dichotomous Outcomes." Annals of the Academy of Medicine, Singapore 38, no. 8 (August 15, 2009): 714–19. http://dx.doi.org/10.47102/annals-acadmedsg.v38n8p714.

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A dichotomous (2-category) outcome variable is often encountered in biomedical research, and Multiple Logistic Regression is often deployed for the analysis of such data. As Logistic Regression estimates the Odds Ratio (OR) as an effect measure, it is only suitable for case-control studies. For cross-sectional and time-to-event studies, the Prevalence Ratio and Cumulative Incidence Ratio can be estimated and easily interpreted. The logistic regression will produce the OR which is difficult to interpret in these studies. In this report, we reviewed 3 alternative multivariate statistical models to replace Logistic Regression for the analysis of data from cross-sectional and time-to-event studies, viz, Modified Cox Proportional Hazard Regression Model, Log-Binomial Regression Model and Poisson Regression Model incorporating the Robust Sandwich Variance. Although none of the models is without flaws, we conclude the last model is the most viable. A numeric example is given to compare the statistical results obtained from all 4 models. Key words: Alternatives to logistic regression, Cross-sectional studies, Risk ratio vs odds ratio

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43

Piccirillo, Jay F., Dennis Fuller, Colin Painter, and John M. Fredrickson. "Multivariate Analysis of Objective Vocal Function." Annals of Otology, Rhinology & Laryngology 107, no. 2 (February 1998): 107–12. http://dx.doi.org/10.1177/000348949810700205.

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No standard and valid multidimensional index of objective voice function has been developed that integrates the information generated from the multiple objective parameters of voice function. The goals of this research were 1) to identify important objective voice parameters and 2) to create a multidimensional voice function index by combining relevant parameters. We evaluated 97 dysphonic patients and 35 normal volunteers on 14 objective voice parameters. Three multidimensional voice indices were created and evaluated: 1) nonweighted univariate index, 2) weighted odds ratio index, and 3) weighted multivariate regression index. The univariate index required all 14 parameters, while the odds ratio and logistic regression models required only 4 parameters (frequency range, airflow at lips, maximum phonation time, and subglottic pressure). The χ2 values for the 3 models were 37.8, 37.6, and 46.0, respectively. All 3 indices were able to satisfactorily classify voice function as normal or abnormal. However, the regression index performed best.

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44

Schuurmans-Stekhoven, James B., and Robert Michael Buckingham. "Ratio or Length?" Journal of Individual Differences 31, no. 3 (January 2010): 150–57. http://dx.doi.org/10.1027/1614-0001/a000023.

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Mounting evidence suggests a link between digit ratio (2D:4D) and personality – albeit with generally small effect sizes. While the identified effects are usually attributed to in utero biochemistry, the bivariate approach favored by researchers in this area does not rule out competing explanations. After first scrutinizing the independence of 2D:4D and overall finger length, we used a multivariate approach to predict the three-dimensional EPQ-R. Given that ring finger length and digit ratio varied conjointly, we introduced a logarithm of residuals model. This new method simultaneously addresses (1) the sensitivity of 2D:4D to measurement error and (2) the high correlations between 2D:4D and finger length. The traditional 2D:4D measure predicted neuroticism but not psychoticism. The logarithm of residuals model was able to identify specific personality effects for ring and index finger residuals. Both neuroticism and psychoticism were related to finger residual measures in a manner consistent with Manning’s in utero biochemistry theory.

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45

Wang, Siqian, Yongyong Ma, Lan Sun, Yifen Shi, Songfu Jiang, Kang Yu, and Shujuan Zhou. "Prognostic Significance of Pretreatment Neutrophil/Lymphocyte Ratio and Platelet/Lymphocyte Ratio in Patients with Diffuse Large B-Cell Lymphoma." BioMed Research International 2018 (December 12, 2018): 1–8. http://dx.doi.org/10.1155/2018/9651254.

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It is generally believed that there is correlation between cancer prognosis and pretreatment PLR and NLR. However, there are limited data about their role in diffuse large B cell lymphoma (DLBCL). This study aims to determine the prognostic value of pretreatment PLR and NLR for patients who have DLBCL. The associations between clinical characteristics and NLR and PLR were evaluated among 182 DLBCL patients from January 2005 to June 2016. The optimal cutoff values for high PLR (⩾150) and NLR (⩾2.32) in prognosis prediction were determined. The effect of NLR and PLR on survival was evaluated through multivariate Cox regression analysis, univariate analysis, and log-rank test. According to the evaluation results, patients with high NLR and PLR had significantly shorter OS (P=0.026 and P=0.035) and PFS (P=0.024 and P=0.022) compared with those who have low PLR and NLR. On multivariate analyses, IPI>2, elevated LDH, and PLR⩾2.32 were prognostic factors for OS and PFS in DLBCL patients. Therefore, we demonstrated that high PLR and NLR predicted adverse prognostic factors in DLBCL patients.

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46

Anderson, T. W., Huang Hsu, and Kai-Tai Fang. "Maximum-likelihood estimates and likelihood-ratio criteria for multivariate elliptically contoured distributions." Canadian Journal of Statistics 14, no. 1 (March 1986): 55–59. http://dx.doi.org/10.2307/3315036.

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47

Melo, Tatiane F. N., Silvia L. P. Ferrari, and Alexandre G. Patriota. "Modified likelihood ratio tests in heteroskedastic multivariate regression models with measurement error." Journal of Statistical Computation and Simulation 84, no. 10 (April 18, 2013): 2233–47. http://dx.doi.org/10.1080/00949655.2013.787691.

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48

Kushner, H. B., Morris Meisner, and Eugene M. Laska. "A bartlett correction factor for a likelihood ratio statistic in multivariate bioassay." Communications in Statistics - Theory and Methods 20, no. 5-6 (January 1991): 1549–55. http://dx.doi.org/10.1080/03610929108830582.

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49

Lai, Tze Leung, and Limin Zhang. "A Modification of schwarz's sequential likelihood ratio tests in multivariate sequential analysis." Sequential Analysis 13, no. 2 (January 1994): 79–96. http://dx.doi.org/10.1080/07474949408836296.

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50

Gomes, M. Ivette. "Generalized Gumbel and likelihood ratio test statistics in the multivariate GEV model." Computational Statistics & Data Analysis 7, no. 3 (February 1989): 259–67. http://dx.doi.org/10.1016/0167-9473(89)90026-1.

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Journal articles on the topic 'Multivariate Ratio'

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