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Journal articles on the topic 'Empirical'

To see the other types of publications on this topic, follow the link: Empirical.

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Chin, G. "Empirically analyzing empirical evidence." Science 349, no. 6251 (August 27, 2015): 939–41. http://dx.doi.org/10.1126/science.349.6251.939-k.

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2

이상원. "Empirical Consequences and Empirical Evidences." Sogang Journal of Philosophy 30, no. ll (August 2012): 39–66. http://dx.doi.org/10.17325/sgjp.2012.30..39.

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3

Déprez, Viviane. "Empirical Quicksand or Empirical Smokescreen?" Journal of Pidgin and Creole Languages 14, no. 2 (December 31, 1999): 371–77. http://dx.doi.org/10.1075/jpcl.14.2.10dep.

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4

Martindale, Colin. "Empirical Questions Deserve Empirical Answers." Philosophy and Literature 20, no. 2 (1996): 347–61. http://dx.doi.org/10.1353/phl.1996.0074.

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5

Hiley, David, and Denise Davidson Greaves. "Empirical." Musical Times 127, no. 1726 (December 1986): 692. http://dx.doi.org/10.2307/964671.

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6

Burch, Druin. "Empirical." Lancet 359, no. 9321 (June 2002): 1954. http://dx.doi.org/10.1016/s0140-6736(02)08776-7.

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7

Baugh, W. M., and D. P. Groeneveld. "Empirical proof of the empirical line." International Journal of Remote Sensing 29, no. 3 (December 21, 2007): 665–72. http://dx.doi.org/10.1080/01431160701352162.

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8

Egger, Joseph, and Mauro Dall'Amico. "Empirical master equations: Numerics." Meteorologische Zeitschrift 16, no. 2 (May 7, 2007): 139–47. http://dx.doi.org/10.1127/0941-2948/2007/0196.

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9

Carrick, Paul. "The Empirical Intelligence -- The Human Empirical Mode." International Studies in Philosophy 24, no. 3 (1992): 133–34. http://dx.doi.org/10.5840/intstudphil1992243128.

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10

Schneider-Mayerson, Matthew, Alexa Weik von Mossner, and W. P. Małecki. "Empirical Ecocriticism: Environmental Texts and Empirical Methods." ISLE: Interdisciplinary Studies in Literature and Environment 27, no. 2 (2020): 327–36. http://dx.doi.org/10.1093/isle/isaa022.

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11

Thomsen, M., and L. Carlsen. "Evaluation of empirical versus non-empirical descriptors." SAR and QSAR in Environmental Research 13, no. 5 (January 2002): 525–40. http://dx.doi.org/10.1080/10629360290023359.

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12

Lea, David. "The Empirical Relevancy of Mental States." South Pacific Journal of Psychology 4 (1991): 6–10. http://dx.doi.org/10.1017/s0257543400001577.

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In this article I defend the following argument: 1) Empirical investigation can only be conducted with reference to statements that are empirically relevant; 2) Statements that refer to mental states cannot be defined so as to render them empirically relevant; 3) Therefore, there can be no empirical investigation of mental states.

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13

Hamori, Shigeyuki. "Empirical Finance." Journal of Risk and Financial Management 13, no. 1 (January 2, 2020): 6. http://dx.doi.org/10.3390/jrfm13010006.

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The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. The content is six articles on machine learning, five articles based on traditional econometric analysis, and five articles on emerging markets. The large share of articles on the application of machine learning is in line with recent trends in finance research. This special issue provides a state-of-the-art overview of empirical finance from economic, financial, and technical points of view.

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14

Vogel, Jonathan, and Alan H. Goldman. "Empirical Knowledge." Philosophical Review 101, no. 2 (April 1992): 428. http://dx.doi.org/10.2307/2185563.

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15

Prosser, Alexander. "Empirical Analysis." Central and Eastern European eDem and eGov Days 342 (March 17, 2022): 177–88. http://dx.doi.org/10.24989/ocg.v.342.6.

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16

LEGET, Carlo, and Pascal BORRY. "Empirical Ethics." Ethical Perspectives 17, no. 2 (June 30, 2010): 231–52. http://dx.doi.org/10.2143/ep.17.2.2049265.

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17

Roll, Richard. "Empirical TIPS." Financial Analysts Journal 60, no. 1 (January 2004): 31–53. http://dx.doi.org/10.2469/faj.v60.n1.2591.

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18

Conee, Earl, and Paul K. Moser. "Empirical Justification." Philosophy and Phenomenological Research 48, no. 3 (March 1988): 563. http://dx.doi.org/10.2307/2107482.

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19

Bonjour, Laurence, and Alan Goldman. "Empirical Knowledge." Philosophy and Phenomenological Research 51, no. 3 (September 1991): 707. http://dx.doi.org/10.2307/2107896.

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20

Ban'kovskaya, Svetlana. "Empirical Phenomenology." Sotsiologicheskoe Obozrenie / Russian Sociological Review 13, no. 1 (2014): 171–75. http://dx.doi.org/10.17323/1728-192x-2014-1-171-175.

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21

Roll, Richard. "Empirical TIPS." CFA Digest 34, no. 3 (August 2004): 87–88. http://dx.doi.org/10.2469/dig.v34.n3.1536.

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22

Day, Timothy Joseph, and Paul K. Moser. "Empirical Justification." Noûs 24, no. 4 (September 1990): 613. http://dx.doi.org/10.2307/2215849.

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23

Luzinski, Craig. "Empirical Outcomes." JONA: The Journal of Nursing Administration 42, no. 4 (April 2012): 185–86. http://dx.doi.org/10.1097/nna.0b013e31824ccc56.

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24

Burell, Gunilla, Arne Öhman, Örjan Sundin, Gunnar Ström, Bengt Ramund, Ingemar Cullhed, and Carl E. Thoresen. "Empirical contributions." International Journal of Behavioral Medicine 1, no. 1 (March 1994): 32–54. http://dx.doi.org/10.1207/s15327558ijbm0101_3.

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25

Ditto, Blaine, Janis France, and Christopher R. France. "Empirical contributions." International Journal of Behavioral Medicine 4, no. 2 (June 1997): 117–30. http://dx.doi.org/10.1207/s15327558ijbm0402_2.

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26

Linton, Steven James, Anna-Lisa Hellsing, and Ing-Liss Bryngelsson. "Empirical contributions." International Journal of Behavioral Medicine 7, no. 4 (December 2000): 291–304. http://dx.doi.org/10.1207/s15327558ijbm0704_02.

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27

Halliday, Robert. "Empirical ethics." Philosophers' Magazine, no. 17 (2002): 17–18. http://dx.doi.org/10.5840/tpm200217114.

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28

Hodgson, Peter E. "Empirical insight." Nature 327, no. 6118 (May 1987): 111–12. http://dx.doi.org/10.1038/327111a0.

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29

Brah, Avtar, Helen Crowley, and Nirmal Puwar. "Empirical interrogations." Feminist Review 78, no. 1 (November 2004): 1–2. http://dx.doi.org/10.1057/palgrave.fr.9400190.

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30

Zhao, Yonggang, and Xiaotong Shen. "Empirical Likelihood." Journal of the American Statistical Association 97, no. 460 (December 2002): 1203–4. http://dx.doi.org/10.1198/jasa.2002.s231.

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31

DeGraff, Michel. "Empirical Quicksand." Journal of Pidgin and Creole Languages 14, no. 2 (December 31, 1999): 359–70. http://dx.doi.org/10.1075/jpcl.14.2.09deg.

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32

Lisa Gorton. "Empirical III." Antipodes 30, no. 2 (2016): 325. http://dx.doi.org/10.13110/antipodes.30.2.0325.

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33

Gilboa, Itzhak, Offer Lieberman, and David Schmeidler. "Empirical Similarity." Review of Economics and Statistics 88, no. 3 (August 2006): 433–44. http://dx.doi.org/10.1162/rest.88.3.433.

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34

Massart, Pascal. "empirical processes." Annals of Probability 28, no. 2 (April 2000): 863–84. http://dx.doi.org/10.1214/aop/1019160263.

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35

Bufacchi, Vittorio. "Empirical Philosophy." International Journal of Applied Philosophy 18, no. 1 (2004): 39–52. http://dx.doi.org/10.5840/ijap20041818.

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36

Sargent, Thomas J., and Christopher A. Sims. "Empirical Macroeconomics." Indian Economic Journal 59, no. 4 (January 2012): 3–33. http://dx.doi.org/10.1177/0019466220120402.

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37

Brown, Harold I. "Empirical testing." Inquiry 38, no. 4 (December 1995): 353–99. http://dx.doi.org/10.1080/00201749508602396.

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38

Nisbet, Alex R. "Empirical Formulas." Journal of Chemical Education 72, no. 7 (July 1995): 668. http://dx.doi.org/10.1021/ed072p668.4.

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39

Bartlett, Peter L., and Shahar Mendelson. "Empirical minimization." Probability Theory and Related Fields 135, no. 3 (September 12, 2005): 311–34. http://dx.doi.org/10.1007/s00440-005-0462-3.

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40

Dalla Chiara, Maria Luisa. "Empirical logics." International Journal of Theoretical Physics 32, no. 10 (October 1993): 1735–46. http://dx.doi.org/10.1007/bf00979497.

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41

Nishimura, Hirokazu. "Empirical sets." International Journal of Theoretical Physics 34, no. 2 (February 1995): 229–52. http://dx.doi.org/10.1007/bf00672804.

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42

Dean, William. "Empirical Theology." Process Studies 19, no. 2 (1990): 85–102. http://dx.doi.org/10.5840/process199019223.

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43

Schölkopf, Bernhard. "Empirical Inference." International Journal of Materials Research 102, no. 7 (July 2011): 809–14. http://dx.doi.org/10.3139/146.110530.

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44

De, Michael. "Empirical Negation." Acta Analytica 28, no. 1 (February 7, 2012): 49–69. http://dx.doi.org/10.1007/s12136-011-0138-9.

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45

d'Espagnat, B. "Empirical reality, empirical causality, and the measurement problem." Foundations of Physics 17, no. 5 (May 1987): 507–29. http://dx.doi.org/10.1007/bf01559699.

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46

Melamed, Benjamin. "The empirical TES methodology: modeling empirical time series." Journal of Applied Mathematics and Stochastic Analysis 10, no. 4 (January 1, 1997): 333–53. http://dx.doi.org/10.1155/s1048953397000403.

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TES (Transform-Expand-Sample) is a versatile class of stochastic sequences defined via an autoregressive scheme with modulo-1 reduction and additional transformations. The scope of TES encompasses a wide variety of sample path behaviors, which in turn give rise to autocorrelation functions with diverse functional forms - monotone, oscillatory, alternating, and others. TES sequences are readily generated on a computer, and their autocorrelation functions can be numerically computed from accurate analytical formulas at a modest computational cost.This paper presents the empirical TES modeling methodology which uses TES process theory to model empirical records. The novel feature of the TES methodology is that it expressly aims to simultaneously capture the empirical marginal distribution (histogram) and autocorrelation function. We draw attention to the non-parametric nature of TES modeling in that it always guarantees an exact match to the empirical marginal distribution. However, fitting the corresponding autocorrelation function calls for a heuristic search for a TES model over a large parametric space. Consequently, practical TES modeling of empirical records must currently rely on software assistance. A visual interactive software environment, called TEStool, has been designed and implemented to support TES modeling. The paper describes the empirical TES modeling methodology as implemented in TEStool and provides numerically-computable formulas for TES autocorrelations. Two examples illustrate the efficacy of the TES modeling approach. These examples serve to highlight the ability of TES models to capture first-order and second-order properties of empirical sample paths and to mimic their qualitative appearance.

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47

Cheng, Y., and Y. Zhao. "Bayesian jackknife empirical likelihood." Biometrika 106, no. 4 (July 13, 2019): 981–88. http://dx.doi.org/10.1093/biomet/asz031.

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Summary Empirical likelihood is a very powerful nonparametric tool that does not require any distributional assumptions. Lazar (2003) showed that in Bayesian inference, if one replaces the usual likelihood with the empirical likelihood, then posterior inference is still valid when the functional of interest is a smooth function of the posterior mean. However, it is not clear whether similar conclusions can be obtained for parameters defined in terms of $U$-statistics. We propose the so-called Bayesian jackknife empirical likelihood, which replaces the likelihood component with the jackknife empirical likelihood. We show, both theoretically and empirically, the validity of the proposed method as a general tool for Bayesian inference. Empirical analysis shows that the small-sample performance of the proposed method is better than its frequentist counterpart. Analysis of a case-control study for pancreatic cancer is used to illustrate the new approach.

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48

Freundlieb, Dieter. "The Empirical Study of Literature. How Empirical can it be?" Empirical Studies of the Arts 7, no. 2 (July 1989): 115–30. http://dx.doi.org/10.2190/5g5l-lltg-081c-8hlm.

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This article addresses some of the problems of an empirical study of literature resulting from the fact that it cannot, qua empirical science, engage in the evaluation of literary texts and the moral issues those texts exemplify as well as the further fact (if it is a fact) that statements about textual meanings in the context of literary interpretations are not empirically true or false. Traditional interpretive literary criticism has always played a significant part in the reproduction and modification of culture. From this point of view, an empirical science of literature must appear severely limited. However, it can be argued that such an empirical study of literature can show that interpretation is necessarily a constructive process and therefore always, to a large extent, determined by (often ideological) background assumptions. An empirical study of literature would make interpretation one of its objects of study and explanation. Such investigations would further our understanding of processes of text comprehension in general, but it would also allow us to reconstruct the background assumptions guiding traditional interpretations.

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49

Ahmed Mareai Senan, Nabil, Anwar Ahmad, Suhaib Anagreh, Mosab I. Tabash, and Eissa A. Al-Homaidi. "An empirical analysis of financial leverage and financial performance: Empirical evidence from Indian listed firms." Investment Management and Financial Innovations 18, no. 2 (June 24, 2021): 322–34. http://dx.doi.org/10.21511/imfi.18(2).2021.26.

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The purpose of this paper is to examine the determinants of financial performance, firm liquidity and financial leverage of Indian listed firms. This study uses both static models (pooled, fixed, and random effects) and Generalized Moment Methods (GMM). Financial leverage (FINLE) is defined by the ratio of total liabilities to total assets, whereas the current ratio and the quick ratio are used as firm liquidity factors. Further, a set of financial performance determinants such as return on assets, profit after tax, return on capital employed, return on equity, and Tobin-Q are used as independent factors. The results indicated that profit after tax, return on equity, return on capital employed, and Tobin-Q are the most significant financial success variables that influence financial leverage of Indian listed companies. Furthermore, profit after tax, return on capital invested, return on equity, and Tobin-Q are considered to have a substantial effect on financial leverage among the financial success indicators. In the case of firm liquidity, the findings show that the current ratio and the quick ratio have a substantial effect on the financial leverage of Indian listed companies.

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50

Zarkasyi, Hamid Fahmy, Jarman Arroisi, Mohammad Syam’un Salim, and Muhammad Taqiyuddin. "Al-attas' Concept of Reality: Empirical and Non-Empirical." KALAM 13, no. 2 (February 7, 2020): 1–30. http://dx.doi.org/10.24042/klm.v13i2.5075.

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