Restoration of the distribution from the mean values of the minima of a random number of random variables

1990 ◽  
Vol 52 (2) ◽  
pp. 2903-2905
Author(s):  
L. B. Klebanov
Keyword(s):  
Random Number ◽  
Random Variables ◽  
Mean Values ◽  
The Mean

scholarly journals On deterministic approximation of Markov processes by ordinary differential equations

1998 ◽  
Vol 4 (2) ◽  
pp. 99-114 ◽  
Author(s):  
L. I. Rozonoer
Keyword(s):  
Differential Equations ◽  
Markov Model ◽  
Ordinary Differential Equations ◽  
Chemical Reaction ◽  
Markov Processes ◽  
Random Variables ◽  
Mean Values ◽  
Multidimensional Lattice ◽  
The Mean

For a class of Markov processes on the integer multidimensional lattice, it is shown that the evolution of the mean values of some random variables can be approximated by ordinary differential equations. To illustrate the approach, a Markov model of a chemical reaction is considered

The maximum and mean of a random length sequence

1992 ◽  
Vol 29 (2) ◽  
pp. 460-466 ◽  
Author(s):  
Peter J. Haas
Keyword(s):  
Limit Theorem ◽  
Joint Distribution ◽  
Random Number ◽  
Finite Interval ◽  
Random Variables ◽  
Convergence Result ◽  
Interval Length ◽  
Sample Mean ◽  
Maximum Value ◽  
The Mean

We obtain a limit theorem for the joint distribution of the maximum value and sample mean of a random length sequence of independent and identically distributed random variables. This extends a previous bivariate convergence result for fixed length sequences and incidentally yields a new proof of Berman's classical limit theorem for the maximum value of a random number of random variables. Our approach uses a property of record time sequences and leads to probabilistically intuitive proofs. We also consider the partition of a finite interval into a random number of subintervals by the points of a non-delayed renewal process. Using the bivariate convergence result for random length sequences, we establish a limit theorem for the joint distribution of the number and maximum length of the subintervals as the interval length becomes large. This leads to limiting results for the ratio of the maximum to the mean subinterval length. Such results are of interest in connection with a simple model of parallel processing.

Limit theorems and estimation theory for branching processes with an increasing random number of ancestors

1997 ◽  
Vol 34 (2) ◽  
pp. 309-327 ◽  
Author(s):  
J. P. Dion ◽  
N. M. Yanev
Keyword(s):  
Limit Theorem ◽  
Limit Theorems ◽  
Random Number ◽  
Branching Processes ◽  
Asymptotic Properties ◽  
Random Variables ◽  
Transfer Theorem ◽  
Asymptotically Normal ◽  
The Mean ◽  
Watson Process

This paper deals with a Bienaymé-Galton-Watson process having a random number of ancestors. Its asymptotic properties are studied when both the number of ancestors and the number of generations tend to infinity. This yields consistent and asymptotically normal estimators of the mean and the offspring distribution of the process. By exhibiting a connection with the BGW process with immigration, all results can be transported to the immigration case, under an appropriate sampling scheme. A key feature of independent interest is a new limit theorem for sums of a random number of random variables, which extends the Gnedenko and Fahim (1969) transfer theorem.

The maximum and mean of a random length sequence

1992 ◽  
Vol 29 (02) ◽  
pp. 460-466
Author(s):  
Peter J. Haas
Keyword(s):  
Limit Theorem ◽  
Joint Distribution ◽  
Random Number ◽  
Finite Interval ◽  
Random Variables ◽  
Convergence Result ◽  
Interval Length ◽  
Sample Mean ◽  
Maximum Value ◽  
The Mean

We obtain a limit theorem for the joint distribution of the maximum value and sample mean of a random length sequence of independent and identically distributed random variables. This extends a previous bivariate convergence result for fixed length sequences and incidentally yields a new proof of Berman's classical limit theorem for the maximum value of a random number of random variables. Our approach uses a property of record time sequences and leads to probabilistically intuitive proofs. We also consider the partition of a finite interval into a random number of subintervals by the points of a non-delayed renewal process. Using the bivariate convergence result for random length sequences, we establish a limit theorem for the joint distribution of the number and maximum length of the subintervals as the interval length becomes large. This leads to limiting results for the ratio of the maximum to the mean subinterval length. Such results are of interest in connection with a simple model of parallel processing.

Use of Prediction Methods to Assess Laboratory Bias and Mean Values Associated with an Interlaboratory Study for Method Validation and/or Proficiency Testing

2014 ◽  
Vol 97 (2) ◽  
pp. 624-629 ◽  
Author(s):  
Foster D McClure ◽  
Jung K Lee
Keyword(s):  
Random Variables ◽  
Least Square ◽  
Best Linear Unbiased Predictor ◽  
Least Square Estimator ◽  
Mean Values ◽  
Interlaboratory Studies ◽  
Repeatability And Reproducibility ◽  
Best Linear Unbiased ◽  
The Mean ◽  
The Individual

Abstract Two methods of prediction of random variables, best predictor (BP) and best linear unbiased predictor (BLUP), are discussed as potential statistical methods to predict laboratory true mean and bias values using the sample laboratory mean (yi) from interlaboratory studies. The predictions developed here require that the interlaboratory and/or proficiency study be designed and conducted in a manner consistent with the assumptions of a one-way completely randomized model (CRM). Under the CRM the individual laboratory true mean and bias are not parameters but are defined to be random variables that are unobservable and considered as realized values that cannot be estimated but can be predicted using methods of “prediction.” The BP method is applicable when all salient parameters are known, e.g., the consensus true overall mean (μ) and repeatability and reproducibility components (σr2 and σR2), while the BLUP method is useful when σ2r and σR2 are known, but μ is estimated by the generalized least square estimator. Although the derivations of predictors are obtained by minimizing the mean-square error under the CRM assumptions, the predictors are the expected laboratory true mean and bias given the sample laboratory mean, i.e., conditional expectation.

Limit theorems and estimation theory for branching processes with an increasing random number of ancestors

1997 ◽  
Vol 34 (02) ◽  
pp. 309-327 ◽  
Author(s):  
J. P. Dion ◽  
N. M. Yanev
Keyword(s):  
Limit Theorem ◽  
Limit Theorems ◽  
Random Number ◽  
Branching Processes ◽  
Asymptotic Properties ◽  
Random Variables ◽  
Transfer Theorem ◽  
Asymptotically Normal ◽  
The Mean ◽  
Watson Process

This paper deals with a Bienaymé-Galton-Watson process having a random number of ancestors. Its asymptotic properties are studied when both the number of ancestors and the number of generations tend to infinity. This yields consistent and asymptotically normal estimators of the mean and the offspring distribution of the process. By exhibiting a connection with the BGW process with immigration, all results can be transported to the immigration case, under an appropriate sampling scheme. A key feature of independent interest is a new limit theorem for sums of a random number of random variables, which extends the Gnedenko and Fahim (1969) transfer theorem.

Reliability Based Optimum Design of Gear Trains

1984 ◽  
Vol 106 (1) ◽  
pp. 17-22 ◽  
Author(s):  
S. S. Rao ◽  
G. Das
Keyword(s):  
Optimum Design ◽  
Bending Strength ◽  
Random Variables ◽  
Kinematic Chain ◽  
Gear Train ◽  
Design Parameters ◽  
Minimum Mass ◽  
Mean Values ◽  
Gear Trains ◽  
The Mean

A reliability based approach is presented for the minimum mass design of gear trains. The gear train is idealized as a weakest link kinematic chain and the optimum design is sought for a specified value of the reliability of the gear train with respect to bending strength and surface wear resistance. The design parameters such as the power transmitted, the geometric dimensions, and the material properties are treated as normally distributed random variables. A linear combination of the mean value and standard deviations of the mass of the gear train is considered as the objective function while treating the mean values of the face widths of the gears as design variables. The reliability based optimization results are compared with those obtained by the deterministic procedure. The effects of variation of parameters like the reliability of the gear train and the coefficients of variation of the random variables are also studied. The minimum mass of the gear trains is found to increase as the specified value of the reliability is increased.

Articulatory Behavior Pre and Post Full-Mouth Tooth Extraction and Alveoloplasty

1980 ◽  
Vol 23 (3) ◽  
pp. 630-645 ◽  
Author(s):  
Gerald Zimmermann ◽  
J.A. Scott Kelso ◽  
Larry Lander
Keyword(s):  
Control Subject ◽  
High Speed ◽  
Tooth Extraction ◽  
Kinematic Parameters ◽  
Experimental Conditions ◽  
Mean Values ◽  
Articulatory Movements ◽  
The Mean ◽  
Experimental Subjects ◽  
Normal Speech

High speed cinefluorography was used to track articulatory movements preceding and following full-mouth tooth extraction and alveoloplasty in two subjects. Films also were made of a control subject on two separate days. The purpose of the study was to determine the effects of dramatically altering the structural dimensions of the oral cavity on the kinematic parameters of speech. The results showed that the experimental subjects performed differently pre and postoperatively though the changes were in different directions for the two subjects. Differences in both means and variabilities of kinematic parameters were larger between days for the experimental (operated) subjects than for the control subject. The results for the Control subject also showed significant differences in the mean values of kinematic variables between days though these day-to-day differences could not account for the effects found pre- and postoperatively. The results of the kinematic analysis, particularly the finding that transition time was most stable over the experimental conditions for the operated subjects, are used to speculate about the coordination of normal speech.

Comparative Study of Intranasal, Subcutaneous and Intravenous Administration of Desamino-D-Arginine Vasopressin (DDAVP)

1986 ◽  
Vol 55 (01) ◽  
pp. 108-111 ◽  
Author(s):  
M Köhler ◽  
P Hellstern ◽  
C Miyashita ◽  
G von Blohn ◽  
E Wenzel
Keyword(s):  
Factor Viii ◽  
Von Willebrand Factor ◽  
Factor Xii ◽  
Additional Advantage ◽  
Mean Values ◽  
Routes Of Administration ◽  
The Mean ◽  
Von Willebrand ◽  
Willebrand Factor ◽  
Factor Antigen

SummaryThis study was performed to evaluate the influence of different routes of administration on the efficacy of DDAVP treatment. Ten healthy volunteers received DDAVP intranasally (i.n.), subcutaneously (s.c.) and intravenously (i.v.) in a randomized cross-over trial. Factor XII and high molecular weight (HMW)-kininogen levels increased only slightly after DDAVP administration. The mean increase of factor VIII: C was 3.1 (i. v.), 2.3 (s. c.), and 1.3 (i.n.) - fold over baseline. Ristocetin cofactor (von Willebrand factor antigen) increased 3.1 (2.5), 2.0 (2.3) and 1.2 (1.2) - fold over baseline mean values after i.v., s.c. and i.n. DDAVP, respectively. The half-disappearance time of factor VIII and von Willebrand factor (vWF) after DDAVP ranged from five (factor VIII: C) to eight hours (vWF). The mean increase of fibrinolytic activity was more pronounced after i.v. DDAVP. The antidiuretic effect was moderate with no apparent differences between the routes of application. This study provides further evidence that both i.v. and s.c. DDAVP administration result in an appropriate and reliable stimulation of haemostasis. An additional advantage of s. c. administration is its suitability for home treatment.

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