super lie groups global topology and local structure a general mathematical framework for the super lie groups of supersymmetric theories is presented the definition of super lie group is given in terms of supermanifolds and two theorems analogous to theorems in classical lie group theory are proved the relationship of the super lie groups defined here to the formal groups of berezin and kac and the graded lie groups of kostant is analyzed

p adic quantum mechanics with p adic valued functions an extension of the formalism of quantum mechanics to the case where the canonical variables and functions are valued in a field of p adic numbers is considered a new p adic integral calculus is used for the realization of the gauss representation in p adic quantum mechanics

nonclassical properties of semi coherent quantum states we study the properties of semi coherent quantum states introduced by mathews and eswaran in 1973 establishing relations between them and other families of nonclassical states such as displaced number states and multiphoton states we also analyse the photon number distribution photon statistics the wigner and marginal distribution functions and show the ways for possible further generalizations

multivariate periodic arma 1 1 processes the main objective of this paper is to investigate the properties of multivariate periodic autoregressive moving average arma l 1 processes such properties include the covariance structure the parameter space and estimation the parameter space of such periodic processes is derived by aggregation for instance monthly models will aggregate to annual models in particular the parameter space and estimation are analyzed for contemporaneous arma carma models it is shown that in general the aggregation of a multivariate periodic arma 1 1 model leads to a stationary multivariate arma 1 1 model however not always a periodic carma 1 1 model aggregates into a stationary carma 1 1 model furthermore it is shown that in estimating the aggregated model parameters the method based on the periodic model and corresponding parameters is superior to that based on the aggregated series

estimation of pearson type 3 moments estimates of frequencies of floods and other events are based on statistical distributions estimated from sample data the most common and generally the most robust technique used is the fitting of data by the method of moments it is well known that moments of the data of an order higher than 1 are biased estimates of the moments of the population from which the data represent a sample much work has been done in deriving factors or functions by which the bias can be corrected all of such work has been based on considering the variation of statistics of various samples drawn from a specified population whereas the real interest is in the range of likely parent population parameters corresponding to a statistic obtained from a unique sample it is recognized that there is no solution to this problem without an assumption of the range and distribution of such parameters as they occur in nature the study described herein examines this question with regard to a particular functional form of parent distribution and for the case where the a priori distributions of the parameters are uniform that is where all values of a particular parameter are equally likely on an arithmetic scale the distribution function studied is the pearson type 3 and the parameter of primary attention is the skew coefficient

product periodic autoregressive processes for modeling intermittent monthly streamflows several attempts have been made in the past to model hydrological processes such as monthly streamflows in dry regions one of the crucial problems in modeling this type of process is the handling of zero flows a stochastic model is presented herein which enables the reproduction of the percentage of zero flows in each month the monthly mean and variance and the month to month correlation of the intermittent flows the model considers the intermittent monthly flow process as a product of a periodic binary discrete process and a periodic continuous process both the discrete and the continuous processes are periodic first order autoregressive parameter estimation has been developed based on the method of moments method of transition probability and method of maximum likelihood

p adic superanalysis ii supermanifolds and differential operators this article is the second part of a work in which p adic supermanifold theory is studied by using the algebraic approach introduced in the first part of this work p adic superdifferential maps are introduced and by restricting attention to the class of strictly differential maps the foundation of p adic supermanifold theory is developed herein in particular it is shown that the superfield expansion theorem is no longer true a superdifferential odd variables map which is not a polynomial is constructed finally tangent space and lie derivatives are constructed and it is shown that no complex valued fermion field of the p adic argument could exist

aggregation and estimation for low order periodic arma models the aggregated time series resulting from summing over the seasons of a seasonal time series which is assumed to follow either an ar 1 or arma 1 1 model having periodic seasonal parameters is shown to follow an arma 1 1 model parameter estimation for such models is investigated analytically and via simulation significant gain in parameter estimation efficiency at the aggregated level is demonstrated when the seasonal data and their model is utilized rather than the aggregated annual data and their model

superspaces and supersymmetries a theory of graded banach modules over a banach grassmann algebra is developed and applied to differential geometry of super manifolds the explicit structure of superspaces carrying poincare supersymmetry and extended supersymmetry including central charges is described

realizing the berezin integral as a superspace contour integral integration on supermanifolds using contours and the covariant differential forms of kostant b kostant graded manifolds graded lie theory and prequantisation in lecture notes in mathematics vol 570 springer berlin 1977 is described the good properties that these integrals naturally acquire are considered it is then shown that the formal process of integration over even and odd variables introduced by berezin f a berezin the method of second quantization academic new york 1966 using partly covariant and partly contravariant forms can be regarded as a special case of these contour integrals over covariant forms

predictor selection for positive autoregressive processes let observations y1 yn be generated from a first order autoregressive ar model with positive errors in both the stationary and unit root cases we derive moment bounds and limiting distributions of an extreme value estimator of the ar coefficient these results enable us to provide asymptotic expressions for the mean squared error mse of and the mean squared prediction error mspe of the corresponding predictor of yn 1 based on these expressions we compare the relative performance of and the least squares predictor estimator from the mspe mse point of view our comparison reveals that the better predictor estimator is determined not only by whether a unit root exists but also by the behavior of the underlying error distribution near the origin and hence is difficult to identify in practice to circumvent this difficulty we suggest choosing the predictor estimator with the smaller accumulated prediction error and show that the predictor estimator chosen in this way is as

poisson brackets as the adelic limit of quantum commutators we show that the adelic limit of the quantum commutator between operators gives a suitable generalization of the poisson bracket on p adic phase space

streamflow simulation a nonparametric approach in this paper kernel estimates of the joint and conditional probability density functions are used to generate synthetic streamflow sequences streamflow is assumed to be a markov process with time dependence characterized by a multivariate probability density function kernel methods are used to estimate this multivariate density function simulation proceeds by sequentially resampling from the conditional density function derived from the kernel estimate of the underlying multivariate probability density function this is a nonparametric method for the synthesis of streamflow that is data driven and avoids prior assumptions as to the form of dependence e g linear or nonlinear and the form of the probability density functions e g gaussian we show using synthetic examples with known underlying models that the nonparametric method presented is more flexible than the conventional models used in stochastic hydrology and is capable of reproducing both linear and nonlinear dependence the effectiveness of this model is illustrated through its application to simulation of monthly streamflow from the beaver river in utah

estimation of arma models with seasonal parameters the yule walker equations for arma p q models with periodic parameters are derived from which moment estimates can be obtained specifically for the case of arma p 1 models the periodic autoregressive parameters can be found by solving a system of linear equations while the periodic moving average parameters satisfy a system of equations which can be solved iteratively particular examples are given and comparisons are made between the proposed moment estimates and estimates given previously

a non gaussian multicomponent model for river flow in order to model satisfactorily sharp rises and slow decreases appearing in any pure runoff time series with a small enough time base exponentially decaying recession and base flow terms are first subtracted from total runoff the remaining part is deseasonalized by a truncated fourier series multiplicator and then modeled as a second order markov stationary series the use of non gaussian distributions allows the modeling of zero contributions which correspond to dry spells an example has been optimized and simulated and the observed and simulated series have been compared even on hydrologically interesting properties such as return periods which were not modeled explicitly the results were satisfactory

physical basis of stochastic models of annual flows the main objective of this paper is to demonstrate that annual streamflow time series can be represented by autoregressive and moving average arma processes the method follows the conceptual representation of a watershed given by fiering 1967

the geometrical framework for yang mills theories we construct a new geometrical framework for yang mills lagrangian field theories as an appropriate quotient space of the standard first jet bundle and investigate the geometrical properties of the resulting mathematical setting we deduce the field equations from a variational problem formulated through a regular poincare cartan form thus ensuring the kinematical admissibility of critical sections we state a generalized nother theorem and explicitly consider the case of the free yang mills field

simple disaggregation by accurate adjusting procedures a multivariate disaggregation method is developed for stochastic simulation of hydrologie series the method is based on three simple ideas that have been proven effective first it starts using directly a typical par 1 model and keeps its formalism and parameter set which is the most parsimonious among linear stochastic models this model is run for the lower level variables without any reference to the known higher level variables second it uses accurate adjusting procedures to allocate the error in the additive property i e the departure of the sum of lower level variables within a period from the corresponding higher level variable they are accurate in the sense that they preserve explicitly certain statistics or even the complete distribution of lower level variables three such procedures have been developed and studied in this paper both theoretically and empirically third it uses repetitive sampling in order to improve the approximations of statistics that are not explicitly preserved by the adjusting procedures the model owing to the wide range of probability distributions it can handle from bell shaped to j shaped and to its multivariate framework is useful for a plethora of hydrologic applications such as disaggregation of annual rainfall or runoff into monthly or weekly amounts and disaggregation of event rainfall depths into partial amounts of hourly or even less duration such real world hydrologic applications have been explored in this study to test the model performance which has proven very satisfactory

periodic covariance stationarity of multivariate periodic autoregressive moving average processes periodic covariance stationarity conditions for multivariate periodic autoregressive moving average parma processes are investigated it follows from a previous work that a necessary and sufficient condition for the periodic stationarity of a multivariate periodic process is the covariance stationarity of the lumped vector process which contains the periodic vectors as its elements it is shown that for univariate and multivariate parma processes even with periodically varying orders the lumped process is a multivariate autoregressive moving average arma process the stationarity conditions of which are readily available periodic stationarity conditions for the multivariate parma 1 1 process are explicitly obtained which apply for all parma 1 q processes it is shown that the periodic stationarity of a periodic process always implies the stationarity of the aggregated process the sum of the periodic vectors the reverse is yet to be proved or disproved however it is shown to be true for par 1 and parma 1 1 processes

general displaced su 1 1 number states revisited the most general displaced number states based on the bosonic and an irreducible representation of the lie algebra symmetry of su 1 1 and associated with the calogero sutherland model are introduced here we utilize the barut girardello displacement operator instead of the klauder perelomov counterpart to construct new kind of the displaced number states which can be classified in nonlinear coherent states regime too with special nonlinearity functions they depend on two parameters and can be converted into the well known barut girardello coherent and number states respectively depending on which of the parameters equal to zero a discussion of the statistical properties of these states is included significant are their squeezing properties and anti bunching effects which can be raised by increasing the energy quantum number depending on the particular choice of the parameters of the above scenario we are able to determine the status of compliance with flexible statistics major parts of the

practical use of analytically derived runoff models based on rainfall point processes this work reports on the practical usage of the four stochastic rainfall runoff models introduced by bierkens and puente 1990 these models which are lumped in space were analytically derived combining two rainfall point process models with two simple runoff parameterizations the rainfall models employ rectangular pulses with poisson prp and neyman scott nsrp arrivals respectively the runoff models route individual rainfall pulses via linear reservoirs one runoff parameterization accounts for surface runoff by using a single linear reservoir slr the other model considers both surface and groundwater runoff employing two linear reservoirs in parallel plr all four representations were tested on two catchments located in the netherlands and colombia emphasis was placed on the models ability to 1 give stable parameter values for data sets at alternative averaging aggregation lengths consistency and 2 preserve historical statistics at alternative averaging lengths when using parameters found from data at other averaging scales robustness results show that the use of alternative rainfall models may have little effect on runoff performance specifically the nsrp model resulted in more robust runoff behavior than the prp model only when combined to the slr parameterization the type of model used to route individual rainfall pulses was found to be important runoff representations containing the plr component were found more robust than those having the slr model

variational calculus on supermanifolds and invariance properties of superspace field theories the foundations of a variational calculus on fibered supermanifolds are outlined giving applications to the formulation of superspace field theories utiyama theorems and conservation laws related to local gauge and general invariance of the theory are proved

a mixed gamma arma 1 1 model for river flow time series in this paper we consider a time series model which can easily be used for simulating stationary river flow sequences with high skewness and the long term correlation structure of an arma 1 1 model the model is fitted to monthly streamflows taken from a river in malaysia the simulated data bear a close resemblance to the historical sequence in terms of the mean variance skewness and autocorrelation coefficients

nonlinear semi coherent states their nonclassical features and phase properties semi coherent states semi css were previously introduced and their nonclassical properties studied in this paper we first propose a general formalism for the construction of nonlinear semi css as an extension of semi css and then their explicit form in the number state basis is generally deduced next as some physical appearances of the formalism after choosing two well known nonlinearity functions nonclassical features and quantum statistical properties of the associated states are studied by evaluating quadrature squeezing higher order squeezing and the mandel parameter as well as their phase properties and number and phase squeezing of the introduced states finally the results are compared with the properties of semi css

nth order minimum uncertainty products for arbitrary n 1 the authors consider the problem of finding the quantum states that minimize the products of the absolute value nth order fluctuation of two canonically conjugate operators a scheme is applied to the problem wherein the exact solution state is approximated by working in a subspace consisting of the ground state plus a finite number of higher order states using a variational technique the solution is first obtained for the ground state and this is subsequently compared to the solution assuming 2 2 and 3 3 spaces for positive n they obtain a small smooth decrease in the value of the fluctuation as the space increases and expect the 3 3 space results to be close to the exact nth order fluctuation for negative n however they get an abrupt decrease in the fluctuation as the space increases

analysis on the p adic superspace i generalized functions gaussian distribution the theory of generalized functions on the p adic superspace qn mp over the super commutative banach superalgebra 0 1 with the trivial 1 annihilator is proposed the p adic gaussian supermeasure is defined as a generalized function on the p adic superspace the connection between the p adic gaussian integration and the p adic gamma function of morita is considered

a stochastic streamflow model based on physical principles based on hydrologic arguments a stochastic model for discretized streamflow from rainfall is proposed in the form of an averaged filtered poisson process fpp using a two component description with excess precipitation and drainage as inputs to the surface and subsurface flow systems simplified models are presented for the physical processes the mean variance and covariance function of discretized streamflow are computed in terms of the first and second moments of excess precipitation and drainage which are derived analytically the behavior of the stochastic properties of streamflow is evaluated for various soil types basin travel times and effective initial soil water contents where there is an intuitive feeling for the interaction of physical characteristics and stochastic properties such as the mean the model appears to produce qualitatively correct results prediction of stochastic properties is limited by the selection of an effective initial soil moisture value

conceptual stochastic modeling of seasonal runoff using autoregressive moving average models and different scales of aggregation the statistical and phenomenological aspects of the runoff process observed on different scales of aggregation are taken as a priori information for the conceptually based stochastic modeling of seasonal runoff runoff is considered as the sum of two groundwater components with over year and subannual response lag and of a purely random component representing the direct runoff this scheme is equivalent to a linear system with two parallel linear reservoirs plus a zero lag linear channel the system output is the runoff and the input is the effective rainfall considered proportional to the direct runoff assuming the effective rainfall as a non gaussian periodic independent process and considering nonseasonal groundwater parameters this conceptualization leads to an autoregressive and moving average 2 2 stochastic process with periodic independent residual stochastic model parameters are directly related to the linear system coefficients and the effective rainfall structure can be determined from the estimated model residual in order to obtain parameter estimates consistent with the conceptual constraints two estimation stages on an annual and a seasonal basis and an iterative procedure are needed the model was applied to a number of time series of monthly streamflows in the apennine regions of italy with promising results

bootstrap inference for a first order autoregression with positive innovations abstract in this article we consider statistical inference for the autoregressive parameter of a first order autoregressive sequence with positive innovations via an extreme value estimator we show that a bootstrap procedure correctly estimates the sampling distribution of an asymptotically pivotal quantity whose distribution depends only on the exponent of regular variation of the innovation distribution based on provided that the ratio of the bootstrap sample size m and the original sample size n converges to zero this result enables us to construct a totally nonparametric confidence interval for the autoregressive parameter we also consider bootstrapping a normalized version of with an application toward bias correction to obtain the bootstrap validity results we develop a continuous convergence result for certain associated point processes we also present results of simulation studies and a numerical example

estimation of seasonal flood risk using a two stage transformation hydrological time series are often asymmetric in time insomuch as rises are more rapid than recessions as well as having highly skewed marginal distributions a two stage transformation is proposed for deseasonalized series rises are stretched and recessions are squashed until the series is symmetric over time an autoregressive moving average arma model is then fitted to the natural logarithms of this new series the residuals from the arma model are represented by a double mixture of weibull and exponential distributions the method is demonstrated with 24 years of daily flows from the river cherwell in the south of england and a 40 year record from the upper reaches of the thames seasonal estimates of flood risk are given and these can be conditioned on catchment wetness at the time of prediction

spacetime locality of the brst formalism the spacetime locality of the brst formalism is investigated the analysis covers gauge theories with either closed or open algebras and is undertaken in the explicit context of the antifield formulation of the brst theory under appropriate conditions the homology of the koszul tate differential modulo the spacetime exterior derivative is shown to be trivial in the space of non integrated densities with positive antighost and pure ghost numbers as a result i the solution of the master equation can be taken to be a local functional ii the gauge fixed action is also a local functional provided one takes the gauge fixing fermion to be a local functional as well and iii the brst transformation is local

p adic superanalysis i infinitely generated banach superalgebras and algebraic groups a sequence of infinitely generated p adic banach superalgebras and their locally convex projective and inductive limits were constructed their spectral properties are characterized herein and by studying the algebras of matrices constructed with these superalgebras an exponential map and its inverse can be defined some examples of superalgebra with pathological spectral properties are discussed

estimation and validation of contemporaneous parma models for streamflow simulation seasonal streamflow series generally exhibit periodicity in the autocovariance structure such periodicity can be represented by parma models i e autoregressive moving average arma models with parameters that vary with the seasons statistical properties of low order models such as the parma 2 2 model are examined the periodic moment equations are derived they can be used to compute the periodic covariance structure of a given model simulation of streamflow at several sites can be done using the contemporaneous parma model the main problem in using such models is to determine the covariance matrices of innovations traditionally this has been done by the method of maximum likelihood however this method generally leads to significant underestimation of the cross correlation of flows a moment estimator is developed herein for the contemporaneous parma 2 2 model along with three approximate moment based estimators for those cases where a feasible moment solution cannot be obtained the applicability of the proposed methods is illustrated by fitting parma models to weekly flow data for two catchments in the ottawa river basin

physical basis of time series models for water table depths the relationship between precipitation excess and water table depth can be described by empirical time series models such as transfer function noise models autoregressive exogenous variable models arx and threshold autoregressive self exciting open loop models in this paper these models are interpreted in terms of the water balance of a soil column a physically based arx model is used to predict the effect of an intervention on the water table dynamics at two locations it is shown that the physically based arx model predicts the effect of interventions reasonably well

on the existence of global integral forms on supermanifolds it is shown that the berezin approach to integration on supermanifolds can be applied to cases where the supermanifold is a twisted extension of a real manifold this is done by showing that supermanifolds admit a subatlas of coordinate charts with transition functions of a quite restricted kind

analytically derived runoff models based on rainfall point processes this work introduces four stochastic and lumped in space rainfall runoff models they arise combining two stochastic rainfall models with two conceptualizations of a basin s response rainfall is given by rectangular pulses whose arrivals are described by poisson and neyman scott models runoff is obtained routing such pulses via two alternative conceptualizations both based on simple linear reservoirs general expressions for the first and second order moments are given for both the instantaneous and locally averaged processes the mathematical structure of the alternative parameterizations is illustrated via computer simulations

the axioms of supermanifolds and a new structure arising from them on presente une analyse des supervarietes sur un algebre arbitraire commutative graduee en partant d un ensemble d axiomes dont le premier est que les derivations de la structure faisceau d une supervariete sont localement libres

new physics in landau levels the most general displaced number coherent states based on the heisenberg su 2 and su 1 1 lie algebras symmetries are constructed they depend on two parameters and can be converted into the well known photon added two variable glauber coherent states and displaced number states respectively depending on which of the parameters is equal to zero the relations of the weyl heisenberg algebra guarantee a corresponding resolution of the identity conditions a discussion of the statistical properties of these states is included significant are their squeezing properties which can be raised by increasing the energy and angular momentum quantum numbers n and m the maximum squeezing is obtained for bext 0 depending on the particular choice of parameters in the above scenarios we are able to determine the status of compliance with poissonian statistics in the limiting case we obtain a major result about the non classical properties of the glauber minimum uncertainty coherent states in other words in addition to the requirement to minimize uncertainty conditions they carry non classical features too finally a theoretical framework is proposed to generate them

consideration of the general state which minimizes the nth order minimum uncertainty product this paper considers the problem of whether mixed states can arise in the nth order minimum uncertainty expression un x n p n using a method due to r jackiw j math phys 9 339 1968 and finding the extremum of the appropriate functional a set of equations is derived which must be satisfied by such a mixed state it is easily shown that a sufficient condition that the equations have a solution is that the mixed state in fact reduces to a pure state it does not appear possible to rigorously prove that such a reduction is necessary although the authors present approximate calculations which make such a conclusion plausible in the course of the approximate calculations generalized nth order squeezed state values for un are also obtained which are of some interest in and of themselves

nth even order minimum uncertainty products this paper considers the problem of finding the quantum states that minimize the products of the even nth order fluctuation of two canonically conjugate operators the problem is first attacked in an abstract form and an equation derived for the desired states a consideration of the n 2 case then leads to a connection with the new concept of squeezed states of the electromagnetic field and the usual exact solution involving the coherent state the concept of higher order squeezing is touched on to motivate the further discussion the cases n 4 6 and 8 are then taken up and approximate solutions to them found via a new numerical technique during these calculations it is noted that only the first few terms in the expansion in number states figure in the solution this observation is then exploited to find an approximate closed solution to the problem valid for all n

a stochastic model for deriving the basic statistics of j day averaged streamflow this study provides the reader with a methodology for directly deriving basic streamflow statistics mean variance and correlation coefficient from long term recorded daily rainfall data a daily streamflow sequence is considered as a filtered point process where the input is a storm time sequence that is assumed to be a marked point process the mark is the storm magnitude that is constructed from a daily rainfall time series and the correlation of the daily rainfall during the storm is considered the number of storms is a counting process represented by either the binomial the poisson or the negative binomial probability distribution depending on its ratio of mean versus variance as a pulse response function for a filtered point process the model of three serial tanks with a parallel tank is adopted to describe the physical process of rainfall runoff thus the basic statistics mean variance and covariance function of j day averaged streamflows can be estimated in terms of the constants expressing stochastic properties of a rainfall time series and the tank model s parameters representing the causal relationship between rainfall and runoff the method is used to derive the streamflow statistics of an actual dam basin the sameura dam basin located in shikoku island japan the resulting computed means and variances of 5 day averaged streamflows show a good correspondence with observed ones

on the body of supermanifolds the problem of constructing the body of a g manifold is considered it is shown that any such manifold is foliated and the body is defined to be the space of the leaves of this foliation under certain regularity conditions on the foliation the body is a smooth finite dimensional real manifold

p adic superanalysis iii the structure of p adic super lie groups this work is the third one of a series of articles in which the foundations of a p adic supermanifold field theory are proposed in this article p adic super lie groups are defined and their properties studied finally the p adic groups of matrices are analyzed

lagrangian supersymmetries depending on derivatives global analysis and cohomology lagrangian contact supersymmetries depending on derivatives of arbitrary order are treated in a very general setting the cohomology of the variational bicomplex on an arbitrary graded manifold and the iterated cohomology of a generic nilpotent contact supersymmetry are computed in particular the first variational formula and conservation laws for lagrangian systems on graded manifolds using contact supersymmetries are obtained

maximum likelihood estimation for periodic autoregressive moving average models a useful class of models for seasonal time series that cannot be filtered or standardized to achieve second order stationarity is that of periodic autoregressive moving average parma models which are extensions of arma models that allow periodic seasonal parameters an approximation to the exact likelihood for gaussian parma processes is developed and a straightforward algorithm for its maximization is presented the algorithm is tested on several periodic arma l 1 models through simulation studies and is compared to moment estimation via the seasonal yule walker equations applicability of the technique is demonstrated through an analysis of a seasonal stream flow series from the rio caroni river in venezuela

periodic gamma autoregressive processes for operational hydrology a number of models have been suggested for hydrologic time series in general and streamflow series in particular most of them are normal autoregressive ar of order 1 with either constant or periodic parameters since generally hydrologic time series are nonnormal skewed transformations have been suggested to make the series approximately normal a new class of univariate models is proposed herein which incorporates skewed and correlation properties within the model structure without the necessity of transformations such models assume a gamma marginal distribution and a constant or periodic autoregressive structure the models may be additive gamma multiplicative gamma or a mixed model which incorporates properties of both additive and multiplicative models the gamma models were tested and compared in relation to transformed normal ar models by computer simulation studies based on five weekly streamflow series with samples varying from 35 to 40 years of record the results show that the new class of gamma models compares favorably with respect to the normal models in reproducing the basic statistics usually analyzed for streamflow simulation it is expected that the proposed gamma models will be of interest to other researchers for further developments and applications to hydrologic and geophysical time series

estimating correlations in multivariate streamflow models multivariate log normal distributions are often used to model and generate multisite and multiseason streamflow sequences a monte carlo study evaluated alternative estimators of the cross correlation between autocorrelated streamflow series and the lag 1 autocorrelation of a single series when some or all of the flows have a log normal distribution generally smaller mean square error estimates of the correlations of flows are obtained by using the variances and covariances of the flow s logarithms to estimate a streamflow model s parameters sometimes it is advantageous to prewhiten two series before calculating their cross correlation and to unbias the estimated autocorrelation of the streamflow s logarithms

variational calculus and poincare cartan formalism on supermanifolds in this work we show how the poincare cartan form can be used to describe the symmetries of lagrangian supersymmetric possibly p adic field theories we show that the existence of the poincare cartan form in supermanifold theory is ensured only in a relevant class of lagrangian densities moreover we give an abstract characterization of supersymmetric invariance based on the poincare cartan form

testing for periodic autocorrelations in seasonal time series data summary this paper presents statistical procedures for deciding if periodicities exist in the autocorrelation function of a seasonal time series after removal of seasonal means and standard deviations the procedures are based on asymptotic properties of a fouriertransformed version of the estimated periodic autocorrelation function analyses of simulated and actual data sets illustrate the usefulness of the proposed methods