
doi: 10.1007/bf02465536
The author gives remainder term estimates for the distribution function \[ F_n(x) = {1 \over n} \cdot \# \Biggl\{m \leq n,\;h_n(m) - \sum_{p \leq n} {h_n(p) \over p} {1 \over s}, \] if \( \beta_{ns} \to 0 \) for some \( s > 2 \), where \[ L_\alpha (F_n, \Phi) = \left( \int^\infty_{-\infty} \left|F_n(x) - \Phi (x) \right|^\alpha dx \right)^{1 \over \alpha}. \] If \( \beta_{n3} \to 0 \), then \[ F_n(x) = \Phi(x) + {x \over 2 \sqrt{2 \pi}} \cdot e^{-{1 \over 2}x^2} \cdot \sum_{p,q\leq n \atop p \cdot q > n} {1 \over pq} \cdot h_n(p) \cdot h_n(q) + O \left( {\beta_{n3} \cdot \log {1 \over \beta_{n3}} \over 1 + x^2} \right), \] and, if \( \beta_{n3} \to 0 \) (as \( n \to \infty \)), \[ L_\alpha (F_n, \Phi) \ll \beta_{n3} \cdot \log {1 \over \beta_{n3}} . \]
Distribution functions associated with additive and positive multiplicative functions, Arithmetic functions in probabilistic number theory, variance of additive functions, central limit theorem, third absolute moment of additive functions, Strongly additive arithmetic function, standard normal distribution, convergence of the value distribution function of strongly additive functions to the normal law in the \( L^p\)-metric
Distribution functions associated with additive and positive multiplicative functions, Arithmetic functions in probabilistic number theory, variance of additive functions, central limit theorem, third absolute moment of additive functions, Strongly additive arithmetic function, standard normal distribution, convergence of the value distribution function of strongly additive functions to the normal law in the \( L^p\)-metric
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