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We connect the theory of local asymptotic normality (LAN) of Le Cam to Boole’s approximation of the Stratonovich stochastic integral by estimating the parameter in the nonlinear drift coefficient of an ergodic diffusion process satisfying a homogeneous Itô stochastic differential equation based on discretely spaced dense observations of the process. The asymptotic normality and local asymptotic minimaxity (in the Hajek–Le Cam sense) of approximate maximum likelihood estimators, approximate maximum probability estimators and approximate Bayes estimators based on Itô and Boole’s approximations of the continuous likelihood are obtained under an almost slowly increasing experimental design (ASIED) condition ( T n 6 / 7 → 0 {\frac{T}{n^{6/7}}\to 0} as T → ∞ {T\to\infty} and n → ∞ {n\to\infty} , where T is the length of the observation time and n is the number of observations) through the weak convergence of the approximate likelihood ratio random fields. Among other things, the Bernstein–von Mises type theorems concerning the convergence of suitably normalized and centered approximate posterior distributions to normal distribution under the same design condition are proved. Asymptotic normality and asymptotic efficiency of the conditional least squares estimator under the same design condition are obtained as a by-product. The log-likelihood derivatives based on Itô approximations are martingales, but the log-likelihood derivatives based on Boole’s approximations are not martingales but weighted averages of forward and backward martingales. These new approximations have faster rate of convergence than the martingale approximations. The methods would have advantages over Euler and Milstein approximations for Monte Carlo simulations.

We study statistical inference for stochastic partial differential equations (SPDEs). Though inference linear SPDEs have been studied well (with lot of problems still remain to be investigated) in the last two decades, inference for nonlinear SPDEs is in its infancy. The inference methods use both inference for finite-dimensional diffusions and inference for classical i.i.d. sequences. Solving 2D Navier-Stokes equation is one of the challenging problem of the last century. However, with additive white noise, the equation has a strong solution. We estimate the viscosity coefficient of the 2D stochastic Navier-Stokes (SNS) equation by minimum contrast method. We show $n$ consistency in contrast to $\sqrt n$ consistency in the classical i.i.d. case where $n$ is the number of observations. We consider both continuous and discrete observations in time. We also obtain the Berry-Esseen bounds. Then we estimate and control the Type I and Type II error of a simple hypothesis testing problem of the viscosity coefficient of the SNS equation. We study a class of rejection regions and provide thresholds that guarantee that the statistical errors are smaller than the given upper bound. The tests are of likelihood ratio type. The proofs are based on the large deviation bounds. Finally we give Monte Carlo test procedure for simulated data.

The paper introduces several approximate maximum likelihood estimators of the parameters of the sub-fractional Chan-Karolyi-Longstaff-Sanders (CKLS) interest rate model and obtains their rates of convergence. A new algorithm inspired by Newton-Cotes formula is presented to improve the accuracy of estimation. The estimators are useful for simulation of interest rates. The proposed new algorithm could be useful for other stochastic computation. It also proposes a generalization of the CKLS interest rate model with sub-fractional Brownian motion drivers which preserves medium range memory.

We obtain explicit form of fine large deviation theorems for the log-likelihood ratio in testing models with fractional nonergodic Ornstein-Uhlenbeck processes with Hurst parameter more than half and get explicit rates of decrease of the error probabilities of Neyman-Pearson, Bayes and minimax tests.

We study recursive parameter estimation in fractional diffusion processes. First, stability and asymptotic properties of the global maximum likelihood estimator (MLE) of the drift parameter are obtained under some regularity conditions. Then we obtain an evolution equation for the MLE of the drift parameter in nonhomogeneous fractional stochastic differential equation (fSDE) driven by fractional Brownian motion. This equation is then modified to yield an algorithm which is consistent, asymptotically efficient and converges to the MLE. The gradient and Newton type algorithm are firstorder approximations. Finally we study the Berry-Esseen inequality for stochastic gradient descent in continuous time (SGDCT) algorithm for American option. We compare it with Longstaff-Schwartz regression based method.

We study the mixingale estimation function estimator of the drift parameter in the stochastic partial differential equation when the process is observed at the arrival times of a Poisson process. We use a two stage estimation procedure. We first estimate the intensity of the Poisson process. Then we substitute this estimate in the estimation function to estimate the drift parameter. We obtain the strong consistency and the asymptotic normality of the mixingale estimation function estimator.

The paper studies the properties of a sequential maximum likelihood estimator of the drift parameter in a one dimensional reflected Ornstein–Uhlenbeck process. We observe the process until the observed Fisher information reaches a specified precision level. We derive the explicit formulas for the sequential estimator and its mean squared error. The estimator is shown to be unbiased and uniformly normally distributed. A simulation study is conducted to assess the performance of the estimator compared with the ordinary maximum likelihood estimator.

We develop higher order accurate estimators of integrated volatility in a stochastic volatility models by using kernel smoothing method and using different weights to kernels. The weights have some relationship to moment problem. As the bandwidth of the kernel vanishes, an estimator of the instantaneous stochastic volatility is obtained. We also develop some new estimators based on smoothing splines.

Usually asset price process has jumps and volatility process has long memory. We study maximum quasi- likelihood estimators for the parameters of a fractionally integrated exponential GARCH, in short FIECO- GARCH process based on discrete observations. We deal with a compound Poisson FIECOGARCH process and study the asymptotic behavior of the maximum quasi-likelihood estimator. We show that the resulting estimators are consistent and asymptotically normal.

Consistency and asymptotic normality of the sieve estimator and an approximate maximum likelihood estimator of the drift coefficient of an interacting particles of diffusions are studied. For the sieve estimator, observations are taken on a fixed time interval [0,T] and asymptotics are studied as the number of interacting particles increases with the dimension of the sieve. For the approximate maximum likelihood estimator, discrete observations are taken in a time interval [0,T] and asymptotics are studied as the number of interacting particles increases with the number of observation time points.

We review the SequentialMonte Carlo (SMC) methods, also known as particle filters, for estimation and pricing in stochastic volatility models with general noises.

Consistency and limit distribution of the maximum likelihood estimator of a parameter in the drift coefficient of an anticipative Skorohod stochastic differential equation satisfying a boundary condition are obtained based on n n independent trajectories of the corresponding Skorohod diffusion inside a time interval [ 0 , T ] [0, T] as n → ∞ n \rightarrow \infty . The results are illustrated for the anticipative Ornstein-Uhlenbeck process.

The paper shows that the distribution of the normalized minimum contrast estimator of the drift parameter in the Ornstein–Uhlenbeck process observed over [0, T] converges to the standard normal distribution with an uniform error rate of the order O (T − 1/2). A precise estimate of the constant in the upper bound is also given.

The paper obtains the explicit form of fine large deviation theorems for the log-likelihood ratio in testing models with fractional Ornstein–Uhlenbeck processes with Hurst parameter bigger than half and obtains the explicit rates of decrease of the error probabilities of Neyman–Pearson, Bayes and minimax tests.

The paper introduces some new approximate minimum contrast estimators of the drift parameter in the Ornstein–Uhlenbeck process based on discretely sampled data and obtains rates of weak convergence of the distributions of the estimators to the standard normal distribution using random, nonrandom and mixed normings.

We study the strong consistency and asymptotic normality of the maximum likelihood estimator of a parameter appearing linearly in the drift coefficient of a partially observed nonlinear stochastic differential system when the observed process is driven by fractional Brownian motion and unobserved (signal process) is driven by ordinary Brownian motion. We illustrate the results for a linear fractional stochastic system.

The Bernstein-von Mises theorem, concerning the convergence of suitably normalized and centred posterior density to normal density, is proved for a certain class of linearly parametrized parabolic stochastic partial differential equations (SPDEs) as the number of Fourier coefficients in the expansion of the solution increases to infinity. As a consequence, the Bayes estimators of the drift parameter, for smooth loss functions and priors, are shown to be strongly consistent, asymptotically normal and locally asymptotically minimax (in the Hajek-Le Cam sense), and asymptotically equivalent to the maximum likelihood estimator as the number of Fourier coefficients become large. Unlike in the classical finite dimensional SDEs, here the total observation time and the intensity of noise remain fixed.

Berry-Esseen bounds, with random and nonrandom normings, and large deviation probability bounds for two approximate maximum likelihood estimators of the drift parameter in the Ornstein-Uhlenbeck process are obtained when the process is observed at equally spaced dense time points. Also obtained are the rates at which these estimators converge to the maximum likelihood estimator based on continuous observation.

Using different random normings, the paper shows that the distributions of the normalized maximum likelihood estimator and normalized regular Bayes estimators of the drift parameter in the Ornstein–Uhlenbeck process observed continuously over [0,T] converge to the standard normal distribution with an error rate O(T−1/2).

Exponential bounds on the large deviation probability of the maximum likelihood estimator and the Bayes estimators of the parameter appearing nonlinearly in the drift coefficient of homogeneous Ito's stochastic differential equations are obtained under some regularity conditions.

Berry-Bsseen bounds with random norming and Jario deviation probabilities arc derived for the maximum likelihood estimator of the drift parameter in tho Ornstoin-Uhlenbeck proccss. AMS (1991) Subject Classification: Primary 62F12, 62M05 Secondary 60FOS, 60F10

The paper is concerned with the approximation of the maximum likelihood estimate of the parameter in the linear drift coefficient of the Ito stochastic differential equation by “trapezodial rule” approximations when observations are made at regularly spaced discrete but very dense time points