925 resultados para Jump-telegraph processes


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Telegraph to H. K. Woodruff of St. Margaret's Island informing him of Samuel DeVeaux Woodruff's death. It is dated July 16, 1918 and signed "Welland".

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Letter to S.D. Woodruff from the Holmes’ Burglar Alarm, Telegraph Company of New York stating that they will send a Mr. Whittaker over to make repairs or changes. There is a partial envelope included with this letter, Feb. 8, 1881.

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Telegraph to S.D. Woodruff from W.H. Johnson of Alpena, Michigan. He says “If I can have the refusal at the twenty five hundred until I can look, you can draw for the seventy two dollars”. The telegraph is torn, Mar. 5, 1880.

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Telegraph to S.D. Woodruff from W. Johnson of Alpena, Michigan stating that he has sent a New York draft for $2,500.00, May 27, 1881.

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Telegram from Great North Western Telegraph Company of Canada to S.D. Woodruff from L. Cabot stating that he will take the shares, Jan. 16, 1886.

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Telegram from Great North Western Telegraph Company of Canada to S.D. Woodruff from L. Cabot stating that the draft for $5000.00 will be forwarded on Monday or Tuesday, Jan. 16, 1886.

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Telegram from Montreal and Dominion Telegraph Companies’ Lines to Louis Cabot stating that Mr. Woodruff has arranged to transfer the shares, Jan. 18, 1886.

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Telegram from Montreal and Dominion Telegraph Companies’ Lines form with a note on it saying “will wait for receipt”[ no sender nor receiver listed, most likely Louis Cabot to Samuel D. Woodruff], 1886.

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Telegraph to Samuel D. Woodruff from T. N. Burrell stating that they do not have enough brocatelle for his order, Sept. 22, 1876.

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Letter to H.H. Collier of the Houston, Texas Telegraph Office from Jim Long of Austin, Texas. Mr. Long writes about the horrible shooting of Mr. James Smith by his overseer, Mr. R. Baker. This letter is torn and taped. A small portion of the bottom of the letter is torn away. This slightly affects the text (2 pages, handwritten), Jan. 25, 1845.

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The Daily Telegraph, London, England (pages 7 and 8). The section” South African Rewards: Honours for the Army” has and x marked beside it. The paper has been taped and there is a hole in one of the pages. This affects the text slightly, April 22, 1901.

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In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

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This paper addresses the issue of estimating semiparametric time series models specified by their conditional mean and conditional variance. We stress the importance of using joint restrictions on the mean and variance. This leads us to take into account the covariance between the mean and the variance and the variance of the variance, that is, the skewness and kurtosis. We establish the direct links between the usual parametric estimation methods, namely, the QMLE, the GMM and the M-estimation. The ususal univariate QMLE is, under non-normality, less efficient than the optimal GMM estimator. However, the bivariate QMLE based on the dependent variable and its square is as efficient as the optimal GMM one. A Monte Carlo analysis confirms the relevance of our approach, in particular, the importance of skewness.