Results 31 to 40 of about 13,246 (161)

Application of Maximum Entropy in Prediction of Common Yew (Taxus baccata L.) potential habitats in the Arasbaran biosphere reserve [PDF]

مجله جنگل ایران, 2020
Common yew (Taxus baccata L.) is classified as an Endangered (En) species and its distribution has been decreased as a long-lived species during last decades, indicating the need for protecting its habitats.
J. Sarhangzadeh, M.R. Elmi
doaj  

Jackknife Estimator of Species Richness with S-PLUS

Journal of Statistical Software, 2005
An estimate of the number of species, S , usually called species richness by ecologists, in an area is one of the basic statistics used to ascertain biological diversity. Traditionally ecologists have used the number of species observed in a sample, S0 ,
Christina D. Smith, Jeffrey S. Pontius
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Comparación de tres métodos indirectos para estimar el crecimiento de la tilapia Oreochromis aureus (Perciformes: Cichlidae) en un lago tropical de México

Revista de Biología Tropical, 2013
Una de las pesquerías de aguas continentales más importantes en México es la pesca de la tilapia Oreochromis aureus. Con base en el análisis de frecuencia de tallas mediante ELEFAN, SLCA y PROJMAT se estimó el crecimiento en la tilapia en el lago de ...
Andrés Arellano-Torres, Daniel Hernández Montaño, Carlos Meléndez Galicia   +2 more
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Euclid preparation

Astronomy & Astrophysics
We develop techniques for generating accurate and precise internal covariances for measurements of clustering and weak-lensing angular power spectra. These methods have been designed to produce non-singular and unbiased covariances for Euclid’s large ...
Naidoo K., Ruiz-Zapatero J., Tessore N., Joachimi B., Loureiro A., Aghanim N., Altieri B., Amara A., Amendola L., Andreon S., Auricchio N., Baccigalupi C., Bagot D., Baldi M., Bardelli S., Battaglia P., Biviano A., Branchini E., Brescia M., Camera S., Capobianco V., Carbone C., Cardone V. F., Carretero J., Castellano M., Castignani G., Cavuoti S., Chambers K. C., Cimatti A., Colodro-Conde C., Congedo G., Conversi L., Copin Y., Courbin F., Courtois H. M., Da Silva A., Degaudenzi H., De Lucia G., Dubath F., Dupac X., Dusini S., Escoffier S., Farina M., Farinelli R., Farrens S., Faustini F., Ferriol S., Finelli F., Fosalba P., Frailis M., Franceschi E., Fumana M., Galeotta S., George K., Gillis B., Giocoli C., Gracia-Carpio J., Grazian A., Grupp F., Holmes W., Hormuth F., Hornstrup A., Jahnke K., Jhabvala M., Keihänen E., Kermiche S., Kiessling A., Kilbinger M., Kubik B., Kümmel M., Kunz M., Kurki-Suonio H., Le Brun A. M. C., Ligori S., Lilje P. B., Lindholm V., Lloro I., Mainetti G., Maino D., Maiorano E., Mansutti O., Marcin S., Marggraf O., Martinelli M., Martinet N., Marulli F., Massey R., Medinaceli E., Mei S., Mellier Y., Meneghetti M., Merlin E., Meylan G., Mora A., Moscardini L., Neissner C., Niemi S.-M., Padilla C., Paltani S., Pasian F., Pedersen K., Percival W. J., Pettorino V., Pires S., Polenta G., Poncet M., Popa L. A., Raison F., Rebolo R., Renzi A., Rhodes J., Riccio G., Romelli E., Roncarelli M., Rosset C., Saglia R., Sakr Z., Sánchez A. G., Sapone D., Sartoris B., Schneider P., Schrabback T., Secroun A., Sefusatti E., Seidel G., Seiffert M., Serrano S., Simon P., Sirignano C., Sirri G., Spurio Mancini A., Stanco L., Steinwagner J., Tallada-Crespí P., Tavagnacco D., Taylor A. N., Tereno I., Toft S., Toledo-Moreo R., Torradeflot F., Tutusaus I., Valenziano L., Valiviita J., Vassallo T., Verdoes Kleijn G., Veropalumbo A., Wang Y., Weller J., Zamorani G., Zerbi F. M., Zucca E., Allevato V., Ballardini M., Bolzonella M., Bozzo E., Burigana C., Cabanac R., Calabrese M., Cappi A., Di Ferdinando D., Escartin Vigo J. A., Gabarra L., Martín-Fleitas J., Matthew S., Mauri N., Metcalf R. B., Pezzotta A., Pöntinen M., Risso I., Scottez V., Sereno M., Tenti M., Viel M., Wiesmann M., Akrami Y., Andika I. T., Anselmi S., Archidiacono M., Atrio-Barandela F., Balaguera-Antolinez A., Bertacca D., Bethermin M., Blanchard A., Blot L., Borgani S., Brown M. L., Bruton S., Calabro A., Camacho Quevedo B., Caro F., Carvalho C. S., Castro T., Cogato F., Conseil S., Cooray A. R., Davini S., Desprez G., Díaz-Sánchez A., Diaz J. J., Di Domizio S., Diego J. M., Dimauro P., Enia A., Fang Y., Ferrari A. G., Ferreira P. G., Finoguenov A., Fontana A., Franco A., Ganga K., García-Bellido J., Gasparetto T., Gautard V., Gaztanaga E., Giacomini F., Gianotti F., Gozaliasl G., Guidi M., Gutierrez C. M., Hall A., Hernández-Monteagudo C., Hildebrandt H., Hjorth J., Joudaki S., Kajava J. J. E., Kang Y., Kansal V., Karagiannis D., Kiiveri K., Kirkpatrick C. C., Kruk S., Lattanzi M., Legrand L., Lembo M., Lepori F., Leroy G., Lesci G. F., Lesgourgues J., Leuzzi L., Liaudat T. I., Macias-Perez J., Maggio G., Magliocchetti M., Mannucci F., Maoli R., Martins C. J. A. P., Maurin L., Miluzio M., Monaco P., Moretti C., Morgante G., Nadathur S., Navarro-Alsina A., Pagano L., Passalacqua F., Paterson K., Patrizii L., Pisani A., Potter D., Quai S., Radovich M., Rocci P.-F., Sacquegna S., Sahlén M., Sanders D. B., Sarpa E., Schneider A., Sciotti D., Sellentin E., Smith L. C., Tanidis K., Testera G., Teyssier R., Tosi S., Troja A., Tucci M., Valieri C., Venhola A., Vergani D., Verza G., Vielzeuf P., Walton N. A.   +281 more
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On the Simulation Study of Jackknife and Bootstrap MSE Estimators of a Domain Mean Predictor for Fay‑Herriot Model

Acta Universitatis Lodziensis. Folia Oeconomica, 2017
We consider the problem of the estimation of the mean squared error (MSE) of some domain mean predictor for Fay‑Herriot model. In the simulation study we analyze properties of eight MSE estimators including estimators based on the jackknife method (Jiang,
Małgorzata Karolina Krzciuk
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Using jackknife to assess the quality of gene order phylogenies

BMC Bioinformatics, 2010
Background In recent years, gene order data has attracted increasing attention from both biologists and computer scientists as a new type of data for phylogenetic analysis.
Luo Haiwei, Zhang Yiwei, Shi Jian, Tang Jijun   +3 more
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Re-sampling in Linear Regression Model Using Jackknife and Bootstrap [PDF]

المجلة العراقية للعلوم الاحصائية, 2010
Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic.
Zakariya Y. Algamal, Khairy B. Rasheed
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Confidence Intervals of Risk Ratios for the Augmented Logistic Regression with Pseudo-Observations

Stats
The augmented logistic regression proposed by Diaz-Quijano directly provides risk ratios with an augmented dataset with the pseudo-observations. However, the standard errors of regression coefficients cannot be accurately estimated using either the ...
Hiroyuki Shiiba, Hisashi Noma
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Crecimiento del pulpo Octopus maya (Mollusca: Cephalopoda) de la costa de Yucatán, México: un análisis de largo plazo

Revista de Biología Tropical, 2001
Los parámetros de crecimiento del pulpo (Octopus maya) de las costas de Yucatán (México) fueron estimados en siete años comprendidos entre 1983-1987, 1989 y 1992. A tales efectos se realizó un análisis de frecuencia de tallas de 19 251 pulpos muestreados
Marta Rocío Nepita Villanueva, Omar Defeo   +1 more
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Jackknife Bias Reduction in the Presence of a Near-Unit Root

Econometrics, 2018
This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root.
Marcus J. Chambers, Maria Kyriacou
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